diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..a6516b1fc187b9b70fead19a65645c0c523c6b59 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,28 @@ +.git +.gitignore +.venv +venv +env +__pycache__ +*.pyc +*.pyo +.pytest_cache +.mypy_cache +.ruff_cache +.tmp +tmp* +pytest_tmp +node_modules +frontend/node_modules +frontend/.next +frontend/out +.vscode +.idea +data +models +training +tests +*.log +.env +.env.local +.env.*.local diff --git a/.github/instructions/copilot-instructions.md b/.github/instructions/copilot-instructions.md new file mode 100644 index 0000000000000000000000000000000000000000..b4cda67aec0f29279e41ed1438a692f55171140d --- /dev/null +++ b/.github/instructions/copilot-instructions.md @@ -0,0 +1,61 @@ +# GitHub Copilot Instructions — GenAI-DeepDetect + +Read CLAUDE.md first. This file adds IDE-specific context. + +## Critical rules (never violate) + +- `src/types.py` is frozen. Never modify `EngineResult`, `DetectionResponse`, or `GeneratorLabel`. +- `NUM_GENERATOR_CLASSES = 8` everywhere. If you see 5 anywhere, fix it to 8. +- SSTGNN checkpoints load with `model.load_state_dict()` only. Never `torch.jit.load`. +- Do not use `torch.jit.script` or `torch.jit.trace` on any PyG model. +- Frontend: pages router only. No `app/` directory. No `src/` wrapper inside `frontend/`. +- All Kaggle training cells call `scripts/train_image_baseline.py`. Not `training/phase1.../train.py` directly. +- `min_detection_confidence=0.3` in all MediaPipe calls. Not 0.5. + +## Generator label index (memorise this) + +``` +0 = real 4 = dall_e +1 = unknown_gan 5 = flux +2 = stable_diffusion 6 = firefly +3 = midjourney 7 = imagen +``` + +## Path conventions + +``` +src/ library code — no sys.exit(), logging only via logging module +scripts/ Kaggle entrypoints — one file per task +training/phase*/ per-phase training scripts +tests/ mirrors src/ structure +configs/ YAML configs per engine +``` + +## Import order for training scripts + +```python +import sys +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # repo root +from src.types import ... +from training.phaseN_*/model import ... +``` + +## Checkpoint format (all engines) + +```python +torch.save({ + "epoch": int, + "model_state": model.state_dict(), + "optimizer": optimizer.state_dict(), + "val_auc": float, + "config": dict, +}, path) +``` + +## Never do these + +- `from training.phase3_sstgnn.model import SSTGNNModel` inside `src/` — use try/except ImportError +- `torch.jit.script(sstgnn_model)` — crashes silently on PyG +- `min_detection_confidence=0.5` — fails on 40% of DFDC frames +- Hardcode `localhost:8000` in frontend — use `process.env.NEXT_PUBLIC_API_URL` +- Import `torch_geometric` at module level without a guard — not available in all envs diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d1b965747d36f5ad5531ab52a6ddcd9d93e3f168 --- /dev/null +++ b/.gitignore @@ -0,0 +1,70 @@ +# GenAI-DeepDetect .gitignore + +# ── Model files ─────────────────────────────────────────────────────────────── +models/ +*.pt +*.pth +*.bin +*.safetensors + +# ── Datasets ────────────────────────────────────────────────────────────────── +data/ +*.zip +*.tar +*.tar.gz + +# ── Cache dirs (never commit these) ────────────────────────────────────────── +.deps-local/ +.pydeps/ +.hf-cache/ +.huggingface/ +__pycache__/ +*.pyc +*.pyo +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ + +# ── Kaggle artifacts ────────────────────────────────────────────────────────── +.kaggle-kernel-output/ +.kaggle-kernel-pulled/ +kaggle.json +# Keep .kaggle-kernel/ (used for packaging) + +# ── Training logs ───────────────────────────────────────────────────────────── +training/logs/ +*.log + +# ── Environment ─────────────────────────────────────────────────────────────── +.env +.env.local +.env.*.local +venv/ +.venv/ +env/ +.env.example + +# ── IDE ─────────────────────────────────────────────────────────────────────── +.vscode/ +.idea/ +*.swp +*.swo +.DS_Store +Thumbs.db + +# ── Frontend ────────────────────────────────────────────────────────────────── +node_modules/ +frontend/node_modules/ +frontend/.next/ +frontend/out/ +frontend/.env.local +frontend/tsconfig.tsbuildinfo + +# ── Junk that got committed before ──────────────────────────────────────────── +"New Text Document.txt" +*.txt.bak + +# ── Build artifacts ─────────────────────────────────────────────────────────── +dist/ +build/ +*.egg-info/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000000000000000000000000000000000000..2735dfa391e68672ac6e0abfbef073083b8c5478 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,1311 @@ +# CLAUDE.md — GenAI-DeepDetect Agent Instructions + +> Read this file before touching any code. It is the single source of truth for +> how this repo is structured, what conventions to follow, and what the hard +> constraints are. + +# CLAUDE.md — GenAI-DeepDetect + +Full implementation guide for AI-assisted development on this project. Read this +file before touching any code. + +-# CLAUDE.md — GenAI-DeepDetect + +Complete implementation guide. Read this before writing any code. All models are +**100% pre-trained** — no training required, no GPU needed locally. + +--- + +## MCP Tools — Always Use These First + +Before writing any code or looking up any API, resolve docs through MCP: + +``` +context7: resolve-library-id + query-docs + → use for: transformers, torch, mediapipe, fastapi, torch-geometric, + google-generativeai, facenet-pytorch, opencv, next.js, runpod + +huggingface: model_search + model_details + hf_doc_search + → use for: finding model cards, checking input formats, confirming + pipeline task names, verifying checkpoint sizes before using +``` + +**Rule**: Never guess an API signature. Always call `context7.query-docs` first. +Never use a HF model without calling `huggingface.model_details` to confirm it +exists, check its license, and verify its input format. + +--- + +## Project Skill And Memory Policy + +For work in this repository, always prefer the installed Claude Code skill pack +when a relevant skill applies instead of ad hoc workflows. + +- Use `context7-mcp` for any library, framework, SDK, or API question, and + before changing code that depends on external packages or hosted services. +- Use `mem-search` / claude-mem whenever the user asks about previous sessions, + prior fixes, earlier decisions, or "how we solved this before". +- When using claude-mem, scope searches to project name `genai-deepdetect` + unless the user explicitly asks for a broader search. +- Keep following the repo-specific MCP rules below even when a general-purpose + skill also applies. + +Recommended companion skills for this project: + +- `systematic-debugging` for bugs, failing tests, or unexpected runtime + behavior +- `verification-before-completion` before claiming a fix is done +- `security-review` for secrets, external APIs, uploads, and auth-sensitive + changes + +--- + +## Project Goal + +Multimodal deepfake and AI-generated content detector. + +- Input: image (JPEG/PNG/WEBP) or video (MP4/MOV/AVI, max 100MB) +- Output: `DetectionResponse` — verdict, confidence, generator attribution, + natural-language explanation, per-engine breakdown + +All inference runs on pre-trained HuggingFace checkpoints. No training scripts +need to run for the system to work. + +--- + +## Architecture + +``` +Request (image/video) + │ + ▼ + FastAPI src/api/main.py + │ + ├── FingerprintEngine (image artifacts, generator attribution) + ├── CoherenceEngine (lip-sync, biological coherence) + └── SSTGNNEngine (landmark spatio-temporal graph) + │ + ▼ + Fuser src/fusion/fuser.py + │ + ▼ + Explainer src/explainability/explainer.py ← Gemini API + │ + ▼ + DetectionResponse src/types.py +``` + +--- + +## All Pre-Trained Models + +Every model downloads via `transformers.pipeline()` or `from_pretrained()`. Zero +training. Zero fine-tuning. + +| Engine | Model | HF ID | Size | Task | +| ----------- | ------------------- | ------------------------------------------ | ------ | ---------------------- | +| Fingerprint | SDXL Detector | `Organika/sdxl-detector` | ~330MB | binary fake/real | +| Fingerprint | CLIP ViT-L/14 | `openai/clip-vit-large-patch14` | ~3.5GB | generator attribution | +| Fingerprint | AI Image Detector | `haywoodsloan/ai-image-detector-deploy` | ~90MB | ensemble backup | +| SSTGNN | DeepFake Detector | `dima806/deepfake_vs_real_image_detection` | ~100MB | ResNet50 per-frame | +| SSTGNN | Deep Fake Detector | `prithivMLmods/Deep-Fake-Detector-Model` | ~80MB | EfficientNet-B4 backup | +| Coherence | MediaPipe Face Mesh | bundled in `mediapipe` package | ~10MB | landmark extraction | +| Coherence | FaceNet VGGFace2 | `facenet-pytorch` (auto-downloads) | ~100MB | temporal embeddings | +| Coherence | SyncNet | `Junhua-Zhu/SyncNet` | ~50MB | lip-sync offset | + +CLIP is the largest at 3.5GB — preload at startup, never reload. Everything else +fits in HF Spaces 16GB RAM free tier. + +--- + +## Environment Variables + +```bash +# Required +GEMINI_API_KEY=... # Google AI Studio — free tier works +HF_TOKEN=hf_... # HuggingFace read token (free) + +# Hosting +RUNPOD_API_KEY=... # RunPod serverless (heavy video) +RUNPOD_ENDPOINT_ID=... # your deployed endpoint ID + +# Paths +MODEL_CACHE_DIR=/data/models # HF Spaces: /data/models (persists) + # local dev: /tmp/models + +# Optional +MAX_VIDEO_FRAMES=300 +MAX_VIDEO_SIZE_MB=100 +INFERENCE_BACKEND=local # "local" | "runpod" +TOKENIZERS_PARALLELISM=false +``` + +Set all secrets in: + +- HF Spaces → Settings → Repository secrets +- RunPod → Secrets tab +- Vercel → Environment Variables + +--- + +## Gemini API — Explainability Engine + +**Primary model**: `gemini-2.5-pro-preview-03-25` **Fallback model**: +`gemini-1.5-pro-002` + +Both available on Google AI Studio free tier (15 req/min, 1M tokens/day). Always +query `context7.query-docs google-generativeai GenerativeModel` before modifying +this file. + +### `src/explainability/explainer.py` + +```python +import os +import logging +import google.generativeai as genai +from src.types import EngineResult + +logger = logging.getLogger(__name__) + +genai.configure(api_key=os.environ["GEMINI_API_KEY"]) + +SYSTEM_INSTRUCTION = ( + "You are a deepfake forensics analyst writing reports for security professionals. " + "Given detection engine outputs, write exactly 2-3 sentences in plain English " + "explaining why the content is real or fake. " + "Be specific — name the strongest signals. " + "Use direct declarative sentences. No hedging. No 'I think'. " + "Output only the explanation text, nothing else." +) + +_model = None + + +def _get_model() -> genai.GenerativeModel: + global _model + if _model is None: + for name in ("gemini-2.5-pro-preview-03-25", "gemini-1.5-pro-002"): + try: + _model = genai.GenerativeModel( + model_name=name, + system_instruction=SYSTEM_INSTRUCTION, + ) + logger.info(f"Gemini model loaded: {name}") + break + except Exception as e: + logger.warning(f"Gemini {name} unavailable: {e}") + return _model + + +def explain( + verdict: str, + confidence: float, + engine_results: list[EngineResult], + generator: str, +) -> str: + breakdown = "\n".join( + f"- {r.engine}: {r.verdict} ({r.confidence:.0%}) — {r.explanation}" + for r in engine_results + ) + prompt = ( + f"Verdict: {verdict} ({confidence:.0%} confidence)\n" + f"Attributed generator: {generator}\n" + f"Engine breakdown:\n{breakdown}\n\n" + "Write the forensics explanation." + ) + try: + model = _get_model() + if model is None: + raise RuntimeError("No Gemini model available") + response = model.generate_content(prompt) + return response.text.strip() + except Exception as e: + logger.error(f"Gemini explain failed: {e}") + top = engine_results[0] if engine_results else None + return ( + f"Content classified as {verdict} with {confidence:.0%} confidence. " + f"{'Primary signal from ' + top.engine + ' engine.' if top else ''}" + ) +``` + +--- + +## Engine Implementations + +### FingerprintEngine — `src/engines/fingerprint/engine.py` + +Query context7 for `transformers pipeline image-classification` and +`huggingface model_details Organika/sdxl-detector` before modifying. + +```python +import os, logging, threading +import numpy as np +from PIL import Image +from transformers import pipeline, CLIPModel, CLIPProcessor +import torch +from src.types import EngineResult + +logger = logging.getLogger(__name__) +CACHE = os.environ.get("MODEL_CACHE_DIR", "/tmp/models") + +GENERATOR_PROMPTS = { + "real": "a real photograph taken by a camera with natural lighting", + "unknown_gan": "a GAN-generated image with checkerboard artifacts and blurry edges", + "stable_diffusion": "a Stable Diffusion image with painterly soft textures", + "midjourney": "a Midjourney image with cinematic dramatic lighting and hyperdetail", + "dall_e": "a DALL-E image with clean illustration-style and smooth gradients", + "flux": "a FLUX model image with photorealistic precision and sharp detail", + "firefly": "an Adobe Firefly image with commercial stock-photo aesthetics", + "imagen": "a Google Imagen image with precise photorealistic rendering", +} + +_lock = threading.Lock() +_detector = _clip_model = _clip_processor = _backup = None + + +def _load(): + global _detector, _clip_model, _clip_processor, _backup + if _detector is not None: + return + logger.info("Loading fingerprint models...") + _detector = pipeline("image-classification", + model="Organika/sdxl-detector", cache_dir=CACHE) + _clip_model = CLIPModel.from_pretrained( + "openai/clip-vit-large-patch14", cache_dir=CACHE) + _clip_processor = CLIPProcessor.from_pretrained( + "openai/clip-vit-large-patch14", cache_dir=CACHE) + _clip_model.eval() + try: + _backup = pipeline("image-classification", + model="haywoodsloan/ai-image-detector-deploy", + cache_dir=CACHE) + except Exception: + logger.warning("Backup fingerprint detector unavailable") + logger.info("Fingerprint models ready") + + +class FingerprintEngine: + + def _ensure(self): + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + self._ensure() + if image.mode != "RGB": + image = image.convert("RGB") + + # Binary fake score + FAKE_LABELS = {"artificial", "fake", "ai-generated", "generated"} + try: + preds = _detector(image) + fake_score = max( + (p["score"] for p in preds if p["label"].lower() in FAKE_LABELS), + default=0.5, + ) + except Exception as e: + logger.warning(f"Primary detector error: {e}") + fake_score = 0.5 + + # Ensemble backup + if _backup is not None: + try: + bp = _backup(image) + bk = max((p["score"] for p in bp + if p["label"].lower() in FAKE_LABELS), default=0.5) + fake_score = fake_score * 0.6 + bk * 0.4 + except Exception: + pass + + # CLIP zero-shot generator attribution + generator = "real" + try: + texts = list(GENERATOR_PROMPTS.values()) + inputs = _clip_processor( + text=texts, images=image, + return_tensors="pt", padding=True, truncation=True, + ) + with torch.no_grad(): + logits = _clip_model(**inputs).logits_per_image[0] + probs = logits.softmax(dim=0).numpy() + generator = list(GENERATOR_PROMPTS.keys())[int(np.argmax(probs))] + except Exception as e: + logger.warning(f"CLIP attribution error: {e}") + + if fake_score > 0.65 and generator == "real": + generator = "unknown_gan" + + return EngineResult( + engine="fingerprint", + verdict="FAKE" if fake_score > 0.5 else "REAL", + confidence=float(fake_score), + attributed_generator=generator, + explanation=f"Binary score {fake_score:.2f}; attributed to {generator}.", + ) + + def run_video(self, frames: list) -> EngineResult: + if not frames: + return EngineResult(engine="fingerprint", verdict="UNKNOWN", + confidence=0.5, explanation="No frames.") + keyframes = frames[::8] or [frames[0]] + results = [self.run(Image.fromarray(f)) for f in keyframes] + avg = float(np.mean([r.confidence for r in results])) + gens = [r.attributed_generator for r in results] + top_gen = max(set(gens), key=gens.count) + return EngineResult( + engine="fingerprint", + verdict="FAKE" if avg > 0.5 else "REAL", + confidence=avg, + attributed_generator=top_gen, + explanation=f"Keyframe average {avg:.2f} over {len(keyframes)} frames.", + ) +``` + +--- + +### CoherenceEngine — `src/engines/coherence/engine.py` + +Query `context7.query-docs mediapipe face_mesh` and +`context7.query-docs facenet-pytorch InceptionResnetV1` before modifying. + +```python +import logging, threading, cv2 +import numpy as np +from PIL import Image +from facenet_pytorch import MTCNN, InceptionResnetV1 +import mediapipe as mp +from src.types import EngineResult + +logger = logging.getLogger(__name__) + +_lock = threading.Lock() +_mtcnn = _resnet = _face_mesh = None + + +def _load(): + global _mtcnn, _resnet, _face_mesh + if _mtcnn is not None: + return + logger.info("Loading coherence models...") + _mtcnn = MTCNN(keep_all=False, device="cpu") + _resnet = InceptionResnetV1(pretrained="vggface2").eval() + _face_mesh = mp.solutions.face_mesh.FaceMesh( + static_image_mode=False, max_num_faces=1, + refine_landmarks=True, min_detection_confidence=0.5, + ) + logger.info("Coherence models ready") + + +class CoherenceEngine: + + def _ensure(self): + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + self._ensure() + frame = np.array(image.convert("RGB")) + score = self._image_score(frame) + return EngineResult( + engine="coherence", + verdict="FAKE" if score > 0.5 else "REAL", + confidence=float(score), + explanation=f"Geometric coherence anomaly {score:.2f} (image mode).", + ) + + def _image_score(self, frame: np.ndarray) -> float: + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if frame.shape[2] == 3 else frame + res = _face_mesh.process(rgb) + if not res.multi_face_landmarks: + return 0.35 # no face detected + + lms = res.multi_face_landmarks[0].landmark + h, w = frame.shape[:2] + + def pt(i): + return np.array([lms[i].x * w, lms[i].y * h]) + + # Eye width asymmetry — deepfakes often mismatched + lew = np.linalg.norm(pt(33) - pt(133)) + rew = np.linalg.norm(pt(362) - pt(263)) + eye_ratio = min(lew, rew) / (max(lew, rew) + 1e-9) + eye_score = max(0.0, (0.85 - eye_ratio) / 0.3) + + # Ear symmetry from nose tip + nose = pt(1) + lr = min(np.linalg.norm(nose - pt(234)), np.linalg.norm(nose - pt(454))) + rr = max(np.linalg.norm(nose - pt(234)), np.linalg.norm(nose - pt(454))) + ear_score = max(0.0, (0.90 - lr / (rr + 1e-9)) / 0.2) + + return float(np.clip(eye_score * 0.5 + ear_score * 0.5, 0.0, 1.0)) + + def run_video(self, frames: list[np.ndarray]) -> EngineResult: + self._ensure() + if len(frames) < 4: + r = self.run(Image.fromarray(frames[0])) + r.explanation = "Too few frames for temporal analysis." + return r + + delta = self._embedding_variance(frames) + jerk = self._landmark_jerk(frames) + blink = self._blink_anomaly(frames) + score = float(np.clip(delta * 0.45 + jerk * 0.35 + blink * 0.20, 0.0, 1.0)) + + return EngineResult( + engine="coherence", + verdict="FAKE" if score > 0.5 else "REAL", + confidence=score, + explanation=( + f"Embedding variance {delta:.2f}, " + f"landmark jerk {jerk:.2f}, " + f"blink anomaly {blink:.2f}." + ), + ) + + def _embedding_variance(self, frames: list[np.ndarray]) -> float: + import torch + embeddings = [] + for frame in frames[::4]: + try: + face = _mtcnn(Image.fromarray(frame)) + if face is not None: + with torch.no_grad(): + e = _resnet(face.unsqueeze(0)).numpy()[0] + embeddings.append(e) + except Exception: + continue + if len(embeddings) < 2: + return 0.5 + deltas = [np.linalg.norm(embeddings[i+1] - embeddings[i]) + for i in range(len(embeddings)-1)] + return float(np.clip(np.var(deltas) * 8, 0.0, 1.0)) + + def _landmark_jerk(self, frames: list[np.ndarray]) -> float: + positions = [] + for frame in frames[::2]: + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + res = _face_mesh.process(rgb) + if res.multi_face_landmarks: + lm = res.multi_face_landmarks[0].landmark + positions.append([lm[1].x, lm[1].y]) + if len(positions) < 4: + return 0.3 + pos = np.array(positions) + jerk = np.diff(pos, n=3, axis=0) + return float(np.clip((np.mean(np.linalg.norm(jerk, axis=1)) - 0.002) / 0.008, + 0.0, 1.0)) + + def _blink_anomaly(self, frames: list[np.ndarray]) -> float: + LEFT_EYE = [33, 160, 158, 133, 153, 144] + RIGHT_EYE = [362, 385, 387, 263, 373, 380] + + def ear(lms, idx, h, w): + pts = [np.array([lms[i].x * w, lms[i].y * h]) for i in idx] + a = np.linalg.norm(pts[1] - pts[5]) + b = np.linalg.norm(pts[2] - pts[4]) + c = np.linalg.norm(pts[0] - pts[3]) + return (a + b) / (2.0 * c + 1e-9) + + ears = [] + for frame in frames: + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + res = _face_mesh.process(rgb) + if res.multi_face_landmarks: + lm = res.multi_face_landmarks[0].landmark + h, w = frame.shape[:2] + ears.append((ear(lm, LEFT_EYE, h, w) + ear(lm, RIGHT_EYE, h, w)) / 2) + + if len(ears) < 10: + return 0.3 + arr = np.array(ears) + blinks = int(np.sum(np.diff((arr < 0.21).astype(int)) > 0)) + bpm = blinks / (len(ears) / 25) * 60 + if 8 <= bpm <= 25: + return 0.15 + if bpm < 3 or bpm > 35: + return 0.80 + return 0.45 +``` + +--- + +### SSTGNNEngine — `src/engines/sstgnn/engine.py` + +Query `context7.query-docs torch-geometric GCNConv` and +`huggingface model_details dima806/deepfake_vs_real_image_detection` before +modifying. + +```python +import logging, os, threading +import numpy as np +import cv2 +from PIL import Image +from transformers import pipeline +import mediapipe as mp +from scipy.spatial import Delaunay +from src.types import EngineResult + +logger = logging.getLogger(__name__) +CACHE = os.environ.get("MODEL_CACHE_DIR", "/tmp/models") + +_lock = threading.Lock() +_det1 = _det2 = _mesh = None + + +def _load(): + global _det1, _det2, _mesh + if _det1 is not None: + return + logger.info("Loading SSTGNN models...") + _det1 = pipeline("image-classification", + model="dima806/deepfake_vs_real_image_detection", + cache_dir=CACHE) + try: + _det2 = pipeline("image-classification", + model="prithivMLmods/Deep-Fake-Detector-Model", + cache_dir=CACHE) + except Exception: + logger.warning("SSTGNN backup detector unavailable") + _mesh = mp.solutions.face_mesh.FaceMesh( + static_image_mode=True, max_num_faces=1, refine_landmarks=True) + logger.info("SSTGNN models ready") + + +def _fake_prob(preds: list[dict]) -> float: + fake_kw = {"fake", "deepfake", "artificial", "generated", "ai"} + return max( + (p["score"] for p in preds + if any(k in p["label"].lower() for k in fake_kw)), + default=0.5, + ) + + +class SSTGNNEngine: + + def _ensure(self): + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + self._ensure() + if image.mode != "RGB": + image = image.convert("RGB") + + scores = [] + try: + scores.append(_fake_prob(_det1(image)) * 0.6) + except Exception as e: + logger.warning(f"SSTGNN det1 error: {e}") + if _det2: + try: + scores.append(_fake_prob(_det2(image)) * 0.4) + except Exception as e: + logger.warning(f"SSTGNN det2 error: {e}") + + if not scores: + return EngineResult(engine="sstgnn", verdict="UNKNOWN", + confidence=0.5, explanation="All detectors failed.") + + cnn = sum(scores) / (0.6 if len(scores) == 1 else 1.0) + graph = self._geometry_score(np.array(image)) + final = float(np.clip(cnn * 0.7 + graph * 0.3, 0.0, 1.0)) + + return EngineResult( + engine="sstgnn", + verdict="FAKE" if final > 0.5 else "REAL", + confidence=final, + explanation=f"CNN {cnn:.2f}, geometric graph anomaly {graph:.2f}.", + ) + + def _geometry_score(self, frame: np.ndarray) -> float: + try: + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + res = _mesh.process(rgb) + if not res.multi_face_landmarks: + return 0.3 + h, w = frame.shape[:2] + lms = res.multi_face_landmarks[0].landmark + idxs = list(range(0, 468, 7))[:68] + pts = np.array([[lms[i].x * w, lms[i].y * h] for i in idxs]) + tri = Delaunay(pts) + areas = [] + for s in tri.simplices: + a, b, c = pts[s] + areas.append(abs(np.cross(b - a, c - a)) / 2) + areas = np.array(areas) + cv_score = float(np.std(areas) / (np.mean(areas) + 1e-9)) + return float(np.clip((cv_score - 0.8) / 1.5, 0.0, 1.0)) + except Exception as e: + logger.warning(f"Geometry score error: {e}") + return 0.3 + + def run_video(self, frames: list[np.ndarray]) -> EngineResult: + self._ensure() + if not frames: + return EngineResult(engine="sstgnn", verdict="UNKNOWN", + confidence=0.5, explanation="No frames.") + sample = frames[::6] or [frames[0]] + results = [self.run(Image.fromarray(f)) for f in sample] + avg = float(np.mean([r.confidence for r in results])) + return EngineResult( + engine="sstgnn", + verdict="FAKE" if avg > 0.5 else "REAL", + confidence=avg, + explanation=f"Frame-sampled SSTGNN average {avg:.2f} over {len(sample)} frames.", + ) +``` + +--- + +## Fusion — `src/fusion/fuser.py` + +```python +import numpy as np +from src.types import EngineResult + +ENGINE_WEIGHTS = { + "fingerprint": 0.45, + "coherence": 0.35, + "sstgnn": 0.20, +} + +ENGINE_WEIGHTS_VIDEO = { + "fingerprint": 0.30, + "coherence": 0.50, + "sstgnn": 0.20, +} + +ATTRIBUTION_PRIORITY = {"fingerprint": 1, "sstgnn": 2, "coherence": 3} + + +def fuse( + results: list[EngineResult], + is_video: bool = False, +) -> tuple[str, float, str]: + """Returns (verdict, confidence, attributed_generator).""" + weights = ENGINE_WEIGHTS_VIDEO if is_video else ENGINE_WEIGHTS + active = [r for r in results if r.verdict != "UNKNOWN"] + + if not active: + return "UNKNOWN", 0.5, "unknown_gan" + + wf = sum(r.confidence * weights.get(r.engine, 0.1) + for r in active if r.verdict == "FAKE") + wr = sum((1 - r.confidence) * weights.get(r.engine, 0.1) + for r in active if r.verdict == "REAL") + + fake_prob = float(np.clip(wf / (wf + wr + 1e-9), 0.0, 1.0)) + verdict = "FAKE" if fake_prob > 0.5 else "REAL" + + generator = "real" + if verdict == "FAKE": + for r in sorted(active, key=lambda r: ATTRIBUTION_PRIORITY.get(r.engine, 9)): + if r.attributed_generator and r.attributed_generator != "real": + generator = r.attributed_generator + break + if generator == "real": + generator = "unknown_gan" + + return verdict, fake_prob, generator +``` + +--- + +## API — `src/api/main.py` + +```python +import asyncio, io, logging, os, time +from pathlib import Path + +import cv2, numpy as np +from fastapi import FastAPI, File, HTTPException, UploadFile +from fastapi.middleware.cors import CORSMiddleware +from PIL import Image + +from src.engines.fingerprint.engine import FingerprintEngine +from src.engines.coherence.engine import CoherenceEngine +from src.engines.sstgnn.engine import SSTGNNEngine +from src.explainability.explainer import explain +from src.fusion.fuser import fuse +from src.services.inference_router import route_inference +from src.types import DetectionResponse + +logger = logging.getLogger(__name__) + +app = FastAPI(title="GenAI-DeepDetect", version="1.0.0") +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], +) + +_fp = FingerprintEngine() +_co = CoherenceEngine() +_st = SSTGNNEngine() + +MAX_MB = int(os.environ.get("MAX_VIDEO_SIZE_MB", 100)) +MAX_FRAMES = int(os.environ.get("MAX_VIDEO_FRAMES", 300)) + +IMAGE_TYPES = {"image/jpeg", "image/png", "image/webp", "image/bmp"} +VIDEO_TYPES = {"video/mp4", "video/quicktime", "video/x-msvideo", "video/webm"} + + +def _extract_frames(path: str) -> list[np.ndarray]: + cap = cv2.VideoCapture(path) + total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + step = max(1, total // MAX_FRAMES) + frames, i = [], 0 + while True: + ret, frame = cap.read() + if not ret: + break + if i % step == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + i += 1 + cap.release() + return frames[:MAX_FRAMES] + + +@app.on_event("startup") +async def preload(): + logger.info("Preloading models...") + await asyncio.gather( + asyncio.to_thread(_fp._ensure), + asyncio.to_thread(_co._ensure), + asyncio.to_thread(_st._ensure), + ) + logger.info("All models preloaded") + + +@app.get("/health") +async def health(): + return {"status": "ok"} + + +@app.post("/detect/image", response_model=DetectionResponse) +async def detect_image(file: UploadFile = File(...)): + t0 = time.monotonic() + if file.content_type not in IMAGE_TYPES: + raise HTTPException(400, f"Unsupported type: {file.content_type}") + data = await file.read() + if len(data) > MAX_MB * 1024 * 1024: + raise HTTPException(413, "File too large") + + image = Image.open(io.BytesIO(data)).convert("RGB") + fp, co, st = await asyncio.gather( + asyncio.to_thread(_fp.run, image), + asyncio.to_thread(_co.run, image), + asyncio.to_thread(_st.run, image), + ) + ms = (time.monotonic() - t0) * 1000 + for r in [fp, co, st]: + r.processing_time_ms = ms + + verdict, conf, gen = fuse([fp, co, st], is_video=False) + expl = await asyncio.to_thread(explain, verdict, conf, [fp, co, st], gen) + + return DetectionResponse( + verdict=verdict, confidence=conf, attributed_generator=gen, + explanation=expl, processing_time_ms=ms, + engine_breakdown=[fp, co, st], + ) + + +@app.post("/detect/video", response_model=DetectionResponse) +async def detect_video(file: UploadFile = File(...)): + t0 = time.monotonic() + if file.content_type not in VIDEO_TYPES: + raise HTTPException(400, f"Unsupported type: {file.content_type}") + data = await file.read() + if len(data) > MAX_MB * 1024 * 1024: + raise HTTPException(413, "File too large") + + # Route heavy videos to RunPod + if len(data) > 20 * 1024 * 1024: + return await route_inference(data, "video") + + tmp = Path(f"/tmp/vid_{int(time.time()*1000)}.mp4") + tmp.write_bytes(data) + try: + frames = await asyncio.to_thread(_extract_frames, str(tmp)) + finally: + tmp.unlink(missing_ok=True) + + if not frames: + raise HTTPException(422, "Could not extract frames") + + fp, co, st = await asyncio.gather( + asyncio.to_thread(_fp.run_video, frames), + asyncio.to_thread(_co.run_video, frames), + asyncio.to_thread(_st.run_video, frames), + ) + ms = (time.monotonic() - t0) * 1000 + for r in [fp, co, st]: + r.processing_time_ms = ms + + verdict, conf, gen = fuse([fp, co, st], is_video=True) + expl = await asyncio.to_thread(explain, verdict, conf, [fp, co, st], gen) + + return DetectionResponse( + verdict=verdict, confidence=conf, attributed_generator=gen, + explanation=expl, processing_time_ms=ms, + engine_breakdown=[fp, co, st], + ) +``` + +--- + +## Types — `src/types.py` + +```python +from __future__ import annotations +from typing import Optional +from pydantic import BaseModel + +GENERATOR_LABELS = { + 0: "real", + 1: "unknown_gan", + 2: "stable_diffusion", + 3: "midjourney", + 4: "dall_e", + 5: "flux", + 6: "firefly", + 7: "imagen", +} + + +class EngineResult(BaseModel): + engine: str + verdict: str # FAKE | REAL | UNKNOWN + confidence: float # 0–1 + attributed_generator: Optional[str] = None + explanation: str = "" + processing_time_ms: float = 0.0 + + +class DetectionResponse(BaseModel): + verdict: str + confidence: float + attributed_generator: str + explanation: str + processing_time_ms: float + engine_breakdown: list[EngineResult] +``` + +--- + +## Inference Router — `src/services/inference_router.py` + +```python +import base64, logging, os +import httpx +from src.types import DetectionResponse + +logger = logging.getLogger(__name__) + +RUNPOD_KEY = os.environ.get("RUNPOD_API_KEY", "") +RUNPOD_EID = os.environ.get("RUNPOD_ENDPOINT_ID", "") + + +async def route_inference(data: bytes, media_type: str) -> DetectionResponse: + if not RUNPOD_KEY or not RUNPOD_EID: + raise RuntimeError( + "RunPod not configured. Set RUNPOD_API_KEY and RUNPOD_ENDPOINT_ID." + ) + url = f"https://api.runpod.ai/v2/{RUNPOD_EID}/runsync" + payload = {"input": {"data": base64.b64encode(data).decode(), + "media_type": media_type}} + async with httpx.AsyncClient(timeout=120) as client: + resp = await client.post(url, json=payload, + headers={"Authorization": f"Bearer {RUNPOD_KEY}"}) + resp.raise_for_status() + return DetectionResponse(**resp.json()["output"]) +``` + +--- + +## RunPod Handler — `runpod_handler.py` (project root) + +```python +import base64, io, os, tempfile +import runpod, cv2, numpy as np +from PIL import Image + +os.environ.setdefault("MODEL_CACHE_DIR", "/tmp/models") + +from src.engines.fingerprint.engine import FingerprintEngine +from src.engines.coherence.engine import CoherenceEngine +from src.engines.sstgnn.engine import SSTGNNEngine +from src.explainability.explainer import explain +from src.fusion.fuser import fuse + +_fp = FingerprintEngine() +_co = CoherenceEngine() +_st = SSTGNNEngine() + + +def handler(job: dict) -> dict: + inp = job["input"] + raw = base64.b64decode(inp["data"]) + media_type = inp.get("media_type", "image") + + if media_type == "image": + image = Image.open(io.BytesIO(raw)).convert("RGB") + fp = _fp.run(image) + co = _co.run(image) + st = _st.run(image) + verdict, conf, gen = fuse([fp, co, st], is_video=False) + else: + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f: + f.write(raw) + tmp = f.name + try: + cap = cv2.VideoCapture(tmp) + frames, i = [], 0 + while True: + ret, frame = cap.read() + if not ret: + break + if i % 4 == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + i += 1 + cap.release() + finally: + os.unlink(tmp) + fp = _fp.run_video(frames) + co = _co.run_video(frames) + st = _st.run_video(frames) + verdict, conf, gen = fuse([fp, co, st], is_video=True) + + expl = explain(verdict, conf, [fp, co, st], gen) + + return { + "verdict": verdict, + "confidence": conf, + "attributed_generator": gen, + "explanation": expl, + "processing_time_ms": 0.0, + "engine_breakdown": [r.model_dump() for r in [fp, co, st]], + } + + +runpod.serverless.start({"handler": handler}) +``` + +--- + +## Hosting + +### Option A — HuggingFace Spaces (Free, CPU, primary API host) + +**`spaces/app.py`**: + +```python +import os +os.environ.setdefault("MODEL_CACHE_DIR", "/data/models") +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") + +import uvicorn +from src.api.main import app + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=7860, workers=1) +``` + +**Root `README.md`** front-matter (Hugging Face reads this file): + +```yaml +--- +title: GenAI DeepDetect +emoji: "🔍" +colorFrom: gray +colorTo: indigo +sdk: docker +app_port: 7860 +pinned: false +--- +``` + +**`Dockerfile`** (replace existing): + +```dockerfile +FROM python:3.11-slim + +RUN apt-get update && apt-get install -y \ + ffmpeg libgl1-mesa-glx libglib2.0-0 libsm6 libxext6 libxrender-dev \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . + +ENV MODEL_CACHE_DIR=/data/models +ENV TOKENIZERS_PARALLELISM=false +ENV PYTHONUNBUFFERED=1 + +EXPOSE 7860 +CMD ["python", "spaces/app.py"] +``` + +**Secrets to set in HF Spaces** (Settings → Repository secrets): + +``` +GEMINI_API_KEY +HF_TOKEN +RUNPOD_API_KEY +RUNPOD_ENDPOINT_ID +``` + +**Free tier**: 2 vCPU, 16GB RAM, persistent `/data` volume. Models cache to +`/data/models` and survive container restarts. Cold start first request: ~90s. +Warm: <5s. GPU upgrade: T4 at $0.05/hr if needed. + +--- + +### Option B — RunPod Serverless (GPU, heavy video, low cost) + +1. RunPod → Serverless → New Endpoint +2. Select template: `runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04` +3. Set handler file: `runpod_handler.py` +4. Min replicas: 0, Max: 3 +5. GPU: RTX 3090 or A40 (cheapest that works) +6. Set env vars: `GEMINI_API_KEY`, `HF_TOKEN`, `MODEL_CACHE_DIR=/tmp/models` + +**Cost**: ~$0.0002/request on H100. Billed per second. Min workers = 0 means you +pay nothing when idle — cold start is ~15s. + +**When it triggers**: `inference_router.py` automatically sends videos >20MB to +RunPod. Images always run on HF Spaces. + +--- + +## Frontend — `frontend/lib/api.ts` + +```typescript +const BASE_URL = + process.env.NEXT_PUBLIC_API_URL ?? + 'https://YOUR-USERNAME-genai-deepdetect.hf.space'; + +export type GeneratorLabel = + | 'real' + | 'unknown_gan' + | 'stable_diffusion' + | 'midjourney' + | 'dall_e' + | 'flux' + | 'firefly' + | 'imagen'; + +export interface EngineResult { + engine: string; + verdict: 'FAKE' | 'REAL' | 'UNKNOWN'; + confidence: number; + attributed_generator: GeneratorLabel | null; + explanation: string; + processing_time_ms: number; +} + +export interface DetectionResponse { + verdict: 'FAKE' | 'REAL' | 'UNKNOWN'; + confidence: number; + attributed_generator: GeneratorLabel; + explanation: string; + processing_time_ms: number; + engine_breakdown: EngineResult[]; +} + +async function _post(endpoint: string, file: File): Promise { + const form = new FormData(); + form.append('file', file); + const res = await fetch(`${BASE_URL}${endpoint}`, { + method: 'POST', + body: form, + }); + if (!res.ok) { + const err = await res.text(); + throw new Error(`Detection failed (${res.status}): ${err}`); + } + return res.json(); +} + +export const detectImage = (file: File) => _post('/detect/image', file); +export const detectVideo = (file: File) => _post('/detect/video', file); +``` + +Set in `frontend/.env.local`: + +``` +NEXT_PUBLIC_API_URL=https://your-username-genai-deepdetect.hf.space +``` + +--- + +## Dependencies — `requirements.txt` + +``` +# API +fastapi>=0.111.0 +uvicorn[standard]>=0.29.0 +python-multipart>=0.0.9 +aiofiles>=23.2.1 +httpx>=0.27.0 +pydantic>=2.7.0 + +# ML — fingerprint +transformers>=4.40.0 +timm>=1.0.0 +torch>=2.1.0 +torchvision>=0.16.0 + +# ML — coherence +facenet-pytorch>=2.5.3 +mediapipe>=0.10.14 +opencv-python-headless>=4.9.0 + +# ML — sstgnn +torch-geometric>=2.5.0 +scipy>=1.13.0 + +# Explainability — Gemini +google-generativeai>=0.8.0 + +# HuggingFace +huggingface-hub>=0.23.0 + +# RunPod serverless handler +runpod>=1.6.0 + +# Continual learning +apscheduler>=3.10.4 + +# Utils +Pillow>=10.3.0 +numpy>=1.26.0 +``` + +--- + +## Bug Checklist — Fix Before Running + +### `src/types.py` + +- [ ] `EngineResult` missing `attributed_generator: Optional[str] = None` — add + it +- [ ] `DetectionResponse.engine_breakdown` typed as `list[dict]` — change to + `list[EngineResult]` + +### `src/fusion/fuser.py` + +- [ ] `fuse()` returns 2-tuple — update to return 3-tuple + `(verdict, conf, generator)` +- [ ] Update all callers in `main.py` accordingly + +### `src/explainability/explainer.py` + +- [ ] References `anthropic` SDK — replace entirely with Gemini implementation + above + +### `src/api/main.py` + +- [ ] Missing CORS middleware — add before deploy +- [ ] Missing `@app.on_event("startup")` preload — add it +- [ ] Missing `_extract_frames()` for video — add it +- [ ] `detect_video` likely missing or stubbed — implement fully + +### `src/engines/*/` directories + +- [ ] All three engine files are stubs or empty — replace with full code above + +### `spaces/app.py` + +- [ ] Likely empty — add uvicorn entrypoint + +### `Dockerfile` + +- [ ] Check for `ffmpeg` and `libgl1-mesa-glx` — required for MediaPipe + OpenCV +- [ ] Check `EXPOSE 7860` matches HF Spaces `app_port` + +### `src/services/inference_router.py` + +- [ ] Likely stub — implement `route_inference()` with RunPod httpx call + +--- + +## Code Standards + +- Lazy-load all models behind a threading lock — never load at module import +- Wrap all model inference in `asyncio.to_thread()` — never block the event loop +- Type hints on every function +- `logging.getLogger(__name__)` not `print()` +- `os.environ.get()` not hardcoded secrets +- Pydantic `BaseModel` for all response schemas +- Next.js: pages router only — no `app/` dir, no `src/` dir +- Font: Plus Jakarta Sans or DM Sans — never Inter, Roboto, Arial +- Border radius: 22% icon containers, 18px cards, 12px buttons + +--- + +## MCP Usage Rules + +Every coding session must follow these rules: + +``` +1. Adding a dependency? + → context7: resolve-library-id + → context7: query-docs + +2. Using any HF model? + → huggingface: model_details + → confirm size, license, task, input format + +3. Modifying engine logic? + → context7: query-docs transformers pipeline (fingerprint) + → context7: query-docs mediapipe face_mesh (coherence) + → context7: query-docs torch-geometric GCNConv (sstgnn) + → context7: query-docs facenet-pytorch (coherence embeddings) + +4. Modifying Gemini calls? + → context7: query-docs google-generativeai GenerativeModel + +5. Modifying RunPod handler? + → context7: query-docs runpod serverless handler + +6. Modifying FastAPI routes? + → context7: query-docs fastapi UploadFile + +7. Frontend API changes? + → context7: query-docs next.js pages-router fetch +``` + +--- + +## Friday Deploy Checklist + +``` +[ ] pip install -r requirements.txt (no errors) +[ ] src/types.py — EngineResult has attributed_generator +[ ] src/types.py — DetectionResponse has engine_breakdown: list[EngineResult] +[ ] src/fusion/fuser.py — returns 3-tuple +[ ] src/explainability/explainer.py — uses Gemini, no anthropic import +[ ] src/engines/fingerprint/engine.py — full implementation +[ ] src/engines/coherence/engine.py — full implementation +[ ] src/engines/sstgnn/engine.py — full implementation +[ ] src/api/main.py — CORS + startup preload + video route +[ ] src/services/inference_router.py — RunPod httpx call +[ ] runpod_handler.py — added to project root +[ ] spaces/app.py — uvicorn entrypoint +[ ] Dockerfile — has ffmpeg, libgl1, EXPOSE 7860 +[ ] HF Space created + secrets set + pushed +[ ] RunPod endpoint deployed + endpoint ID noted +[ ] frontend/.env.local — NEXT_PUBLIC_API_URL points to HF Space +[ ] Vercel deploy of frontend/ + +Smoke tests: +[ ] GET /health → {"status":"ok"} +[ ] POST /detect/image (real JPEG) → verdict REAL +[ ] POST /detect/image (AI PNG) → verdict FAKE +[ ] POST /detect/video (MP4 <20MB) → response within 30s +[ ] POST /detect/video (MP4 >20MB) → routes to RunPod +``` + diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..535945b48bafa5b74c277e8f24153bc708b6d8d4 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.11-slim + +RUN apt-get update && apt-get install -y \ + ffmpeg libgl1-mesa-glx libglib2.0-0 libsm6 libxext6 libxrender-dev \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . + +ENV MODEL_CACHE_DIR=/data/models +ENV TOKENIZERS_PARALLELISM=false +ENV PYTHONUNBUFFERED=1 + +EXPOSE 7860 +CMD ["python", "spaces/app.py"] diff --git a/README.md b/README.md index 999bc2e64fddc5f954a3882439b8203fe0784377..d54a72ceb4f8c71122d6cf7b0946320f54a14c4a 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,19 @@ --- -title: Deepdetection -emoji: 📈 -colorFrom: indigo -colorTo: purple +title: GenAI DeepDetect +emoji: "??" +colorFrom: gray +colorTo: indigo sdk: docker +app_port: 7860 pinned: false -license: mit -short_description: A --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# GenAI-DeepDetect + +Docker-based Hugging Face Space for multimodal deepfake detection. + +This Space runs the FastAPI service from `src/api/main.py` and exposes: + +- `GET /health` +- `POST /detect/image` +- `POST /detect/video` diff --git a/configs/coherence_baseline.yaml b/configs/coherence_baseline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..066e9cfe985d949bb34bf42dc3f7364534e14706 --- /dev/null +++ b/configs/coherence_baseline.yaml @@ -0,0 +1,44 @@ +# configs/coherence_baseline.yaml +# Training config for the Coherence (lip-audio sync) engine — Phase 2 + +engine: coherence + +kaggle_datasets: + - xhlulu/faceforensics-in-compressed-videos + - reubensinclair/celeb-df + +kaggle_competitions: + - deepfake-detection-challenge # DFDC — requires competition acceptance + +data: + processed_dir: /kaggle/working/processed/coherence + sources: + - mount: /kaggle/input/faceforensics-in-compressed-videos + - mount: /kaggle/input/deepfake-detection-challenge + - mount: /kaggle/input/celeb-df + +preprocess_args: + workers: 4 + max_clips: 20 + +model: + visual_stream: resnet18_truncated_layer3 + audio_stream: 1d_cnn + embed_dim: 256 + dropout: 0.3 + +train_args: + data_dir: /kaggle/working/processed/coherence + output_dir: /kaggle/working/checkpoints/coherence + epochs: 25 + batch_size: 16 + lr: 1.0e-4 + weight_decay: 1.0e-4 + contrastive_weight: 0.1 + amp: true + seed: 42 + patience: 5 + +targets: + val_auc: 0.88 + eer: 0.15 diff --git a/configs/fingerprint_baseline.yaml b/configs/fingerprint_baseline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ab716815d427564265d93dba25009a6cadf43377 --- /dev/null +++ b/configs/fingerprint_baseline.yaml @@ -0,0 +1,46 @@ +# configs/fingerprint_baseline.yaml +# Training config for the Fingerprint (ViT) engine — Phase 1 + +engine: fingerprint + +kaggle_datasets: + - xhlulu/140k-real-and-fake-faces + - philosopher0/ai-generated-vs-real-images-datasaet + +kaggle_competitions: [] + +data: + processed_dir: /kaggle/working/processed/fingerprint + sources: + - slug: xhlulu/140k-real-and-fake-faces + mount: /kaggle/input/140k-real-and-fake-faces + - slug: philosopher0/ai-generated-vs-real-images-datasaet + mount: /kaggle/input/ai-generated-vs-real-images-datasaet + +model: + name: vit_base_patch16_224 + pretrained: true + num_binary_classes: 2 + num_generator_classes: 8 # must match GENERATOR_CLASSES in src/training/config.py + +train_args: + data_dir: /kaggle/working/processed/fingerprint + output_dir: /kaggle/working/checkpoints/fingerprint + epochs: 30 + batch_size: 64 + lr: 2.0e-5 + weight_decay: 0.01 + warmup_steps: 500 + grad_accum: 2 + amp: true + seed: 42 + patience: 5 + +demo_args: + epochs: 5 + batch_size: 64 + +targets: + val_auc: 0.92 + test_auc: 0.90 + cross_dataset_auc: 0.80 diff --git a/configs/sstgnn_baseline.yaml b/configs/sstgnn_baseline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9a38ad73d70b27e3b9890bfdc5c00a753defff6 --- /dev/null +++ b/configs/sstgnn_baseline.yaml @@ -0,0 +1,49 @@ +# configs/sstgnn_baseline.yaml +# Training config for the SSTGNN (graph attention) engine — Phase 3 +# +# IMPORTANT: use T4x2 accelerator, NOT P100. +# Do NOT set amp: true — mixed precision causes NaN in GAT layers. + +engine: sstgnn + +kaggle_datasets: + - xhlulu/faceforensics-in-compressed-videos + - reubensinclair/celeb-df + +kaggle_competitions: + - deepfake-detection-challenge + +data: + processed_dir: /kaggle/working/processed/sstgnn + +landmark_args: + window_frames: 64 + window_stride: 32 + num_landmarks: 68 + min_detection_confidence: 0.3 # FIX: was 0.5 — too strict for DFDC low-res + min_tracking_confidence: 0.3 + min_video_height: 240 + +model: + in_channels: 5 + hidden_dim: 64 + heads: 4 + num_gat_layers: 3 + dropout: 0.3 + out_dropout: 0.4 + +train_args: + data_dir: /kaggle/working/processed/sstgnn + output_dir: /kaggle/working/checkpoints/sstgnn + epochs: 40 + batch_size: 8 + lr: 5.0e-4 + weight_decay: 5.0e-4 + scheduler: cosine + seed: 42 + patience: 8 + amp: false # NEVER set true for SSTGNN + +targets: + val_auc: 0.85 + test_auc: 0.83 diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a982bae08467197997d560b0e5e92fd453bb0448 --- /dev/null +++ b/frontend/Dockerfile @@ -0,0 +1,25 @@ +# Stage 1 — install deps +FROM node:20-alpine AS deps +WORKDIR /app +COPY package.json package-lock.json* ./ +RUN npm ci + +# Stage 2 — build +FROM node:20-alpine AS builder +WORKDIR /app +COPY --from=deps /app/node_modules ./node_modules +COPY . . +RUN npm run build + +# Stage 3 — runtime +FROM node:20-alpine AS runner +WORKDIR /app +ENV NODE_ENV=production + +COPY --from=builder /app/.next ./.next +COPY --from=builder /app/node_modules ./node_modules +COPY --from=builder /app/package.json ./package.json +COPY --from=builder /app/public ./public + +EXPOSE 3000 +CMD ["npm", "start"] diff --git a/frontend/components/ui/background-paths.tsx b/frontend/components/ui/background-paths.tsx new file mode 100644 index 0000000000000000000000000000000000000000..88f9acd60d0a3154f4439fcdc801181f382619a3 --- /dev/null +++ b/frontend/components/ui/background-paths.tsx @@ -0,0 +1,50 @@ +'use client' + +interface Props { + opacity?: number +} + +export default function BackgroundPaths({ opacity = 0.04 }: Props) { + return ( + + ) +} diff --git a/frontend/components/ui/confidence-ring.tsx b/frontend/components/ui/confidence-ring.tsx new file mode 100644 index 0000000000000000000000000000000000000000..73e4c292856aff4a240d5bf5f774230a7df7c6d6 --- /dev/null +++ b/frontend/components/ui/confidence-ring.tsx @@ -0,0 +1,67 @@ +import { motion } from 'framer-motion' +import type { Verdict } from '@/types/detection' +import { formatConfidence } from '@/lib/utils' + +interface Props { + confidence: number + verdict: Verdict +} + +const RADIUS = 36 +const CIRCUMFERENCE = 2 * Math.PI * RADIUS + +export default function ConfidenceRing({ confidence, verdict }: Props) { + const dashOffset = CIRCUMFERENCE * (1 - confidence) + const color = verdict === 'FAKE' ? '#ef4444' : '#22c55e' + const bgColor = verdict === 'FAKE' ? 'rgba(239,68,68,0.1)' : 'rgba(34,197,94,0.08)' + + return ( +
+ {/* SVG ring */} +
+ + {/* Track */} + + {/* Progress */} + + +
+ + {Math.round(confidence * 100)}% + +
+
+ +
+
+ Confidence +
+
+ {formatConfidence(confidence)} +
+
+ overall {verdict.toLowerCase()} confidence +
+
+
+ ) +} diff --git a/frontend/components/ui/engine-breakdown.tsx b/frontend/components/ui/engine-breakdown.tsx new file mode 100644 index 0000000000000000000000000000000000000000..2d7927b1867c732ba6baad71d84397e09d29c12c --- /dev/null +++ b/frontend/components/ui/engine-breakdown.tsx @@ -0,0 +1,66 @@ +import { motion } from 'framer-motion' +import type { EngineResult } from '@/types/detection' +import VerdictBadge from './verdict-badge' +import { ENGINE_LABELS } from '@/lib/constants' +import { formatMs } from '@/lib/utils' + +interface Props { + engines: EngineResult[] +} + +export default function EngineBreakdown({ engines }: Props) { + return ( +
+ {engines.map((engine, i) => { + const isNA = engine.explanation.startsWith('N/A') + return ( + + {/* Engine name */} + + {ENGINE_LABELS[engine.engine] ?? engine.engine.toUpperCase()} + + + {/* Verdict badge or NA */} + {isNA ? ( + + VIDEO ONLY + + ) : ( + + )} + + {/* Confidence bar */} +
+ {!isNA && ( +
+ +
+ )} +
+ + {/* Score */} + + {isNA ? '—' : `${Math.round(engine.confidence * 100)}%`} + + + {/* Time */} + + {engine.processing_time_ms > 0 ? formatMs(engine.processing_time_ms) : '—'} + + + ) + })} +
+ ) +} diff --git a/frontend/components/ui/liquid-glass-button.tsx b/frontend/components/ui/liquid-glass-button.tsx new file mode 100644 index 0000000000000000000000000000000000000000..805c052d8ec594264225e52a52fe3a02c1262b82 --- /dev/null +++ b/frontend/components/ui/liquid-glass-button.tsx @@ -0,0 +1,31 @@ +import { cn } from '@/lib/utils' +import type { ButtonHTMLAttributes } from 'react' + +interface Props extends ButtonHTMLAttributes { + variant?: 'primary' | 'ghost' + size?: 'sm' | 'md' +} + +export default function LiquidGlassButton({ + children, + variant = 'primary', + size = 'md', + className, + ...rest +}: Props) { + return ( + + ) +} diff --git a/frontend/components/ui/loading-orbit.tsx b/frontend/components/ui/loading-orbit.tsx new file mode 100644 index 0000000000000000000000000000000000000000..fe0691cb23eb2d8cabdcf2e7f029e9e2eb05113a --- /dev/null +++ b/frontend/components/ui/loading-orbit.tsx @@ -0,0 +1,67 @@ +import { useState, useEffect } from 'react' +import { motion, AnimatePresence } from 'framer-motion' +import { LOADING_STAGES } from '@/lib/constants' + +export default function LoadingOrbit() { + const [stageIndex, setStageIndex] = useState(0) + + useEffect(() => { + const id = setInterval(() => { + setStageIndex(i => (i + 1) % LOADING_STAGES.length) + }, 2800) + return () => clearInterval(id) + }, []) + + return ( +
+ {/* Spinning ring */} +
+ {/* Static inner circle */} +
+ {/* Outer orbit ring */} + + {/* Center label */} + + SCANNING + +
+ + {/* Stage label — animates on change */} + + + {LOADING_STAGES[stageIndex]} + + + + {/* Dots */} +
+ {LOADING_STAGES.map((_, i) => ( +
+ ))} +
+
+ ) +} diff --git a/frontend/components/ui/media-meta-strip.tsx b/frontend/components/ui/media-meta-strip.tsx new file mode 100644 index 0000000000000000000000000000000000000000..092275230e82a8d015f19eddd0961383ea62b483 --- /dev/null +++ b/frontend/components/ui/media-meta-strip.tsx @@ -0,0 +1,23 @@ +import { formatBytes } from '@/lib/utils' + +interface Props { + file: File +} + +export default function MediaMetaStrip({ file }: Props) { + const ext = file.name.split('.').pop()?.toUpperCase() ?? '—' + + return ( +
+ + File + +
+

{file.name}

+

+ {ext} · {formatBytes(file.size)} +

+
+
+ ) +} diff --git a/frontend/components/ui/radial-orbital-timeline.tsx b/frontend/components/ui/radial-orbital-timeline.tsx new file mode 100644 index 0000000000000000000000000000000000000000..41c425e73cad65bde9469a552cee40a2559ec320 --- /dev/null +++ b/frontend/components/ui/radial-orbital-timeline.tsx @@ -0,0 +1,73 @@ +import type { LucideIcon } from 'lucide-react' + +export interface OrbitalNode { + label: string + Icon: LucideIcon +} + +interface Props { + nodes: OrbitalNode[] + centerLabel?: string + radius?: number + size?: number +} + +function nodePosition(index: number, total: number, radius: number) { + const angle = (Math.PI / 2) - (index * 2 * Math.PI) / total + return { + x: Math.round(radius * Math.cos(angle)), + y: Math.round(-radius * Math.sin(angle)), + } +} + +export default function RadialOrbitalTimeline({ + nodes, + centerLabel = 'DETECT', + radius = 140, + size = 320, +}: Props) { + return ( +
+ {/* Orbit rings */} + {[size * 0.875, size * 0.625].map((d, i) => ( +
+ ))} + + {/* Center */} +
+ + GENAI
{centerLabel} + +
+ + {/* Nodes */} + {nodes.map((node, i) => { + const pos = nodePosition(i, nodes.length, radius) + return ( +
+
+ +
+ + {node.label} + +
+ ) + })} +
+ ) +} diff --git a/frontend/components/ui/upload-zone.tsx b/frontend/components/ui/upload-zone.tsx new file mode 100644 index 0000000000000000000000000000000000000000..4cb6867ee3630468f3ddcfb7ae0503cec7090b45 --- /dev/null +++ b/frontend/components/ui/upload-zone.tsx @@ -0,0 +1,96 @@ +import { useRef, useState, useCallback } from 'react' +import { Image as ImageIcon, Film } from 'lucide-react' +import { cn } from '@/lib/utils' +import { + ALLOWED_IMAGE_TYPES, ALLOWED_VIDEO_TYPES, + IMAGE_FORMAT_LABEL, VIDEO_FORMAT_LABEL, + MAX_IMAGE_MB, MAX_VIDEO_MB, +} from '@/lib/constants' + +interface Props { + mediaType: 'image' | 'video' + onFile: (file: File) => void +} + +export default function UploadZone({ mediaType, onFile }: Props) { + const inputRef = useRef(null) + const [dragging, setDragging] = useState(false) + const [error, setError] = useState(null) + + const accept = mediaType === 'image' ? ALLOWED_IMAGE_TYPES.join(',') : ALLOWED_VIDEO_TYPES.join(',') + const formatLabel = mediaType === 'image' ? IMAGE_FORMAT_LABEL : VIDEO_FORMAT_LABEL + const maxMb = mediaType === 'image' ? MAX_IMAGE_MB : MAX_VIDEO_MB + const Icon = mediaType === 'image' ? ImageIcon : Film + + const validate = useCallback((file: File): string | null => { + const allowed = mediaType === 'image' ? ALLOWED_IMAGE_TYPES : ALLOWED_VIDEO_TYPES + if (file.type && !allowed.includes(file.type)) { + return `Unsupported format. Allowed: ${formatLabel}` + } + if (file.size > maxMb * 1024 * 1024) { + return `File too large. Max ${maxMb}MB.` + } + return null + }, [mediaType, formatLabel, maxMb]) + + const handleFiles = useCallback((files: FileList | null) => { + if (!files || files.length === 0) return + const file = files[0] + const err = validate(file) + if (err) { setError(err); return } + setError(null) + onFile(file) + }, [validate, onFile]) + + const onDrop = (e: React.DragEvent) => { + e.preventDefault() + setDragging(false) + handleFiles(e.dataTransfer.files) + } + + return ( +
+ + + {error && ( +

+ {error} +

+ )} +
+ ) +} diff --git a/frontend/components/ui/verdict-badge.tsx b/frontend/components/ui/verdict-badge.tsx new file mode 100644 index 0000000000000000000000000000000000000000..87ca805b58426ebf00d64c222e6f878ac2339d6e --- /dev/null +++ b/frontend/components/ui/verdict-badge.tsx @@ -0,0 +1,23 @@ +import type { Verdict } from '@/types/detection' +import { cn } from '@/lib/utils' + +interface Props { + verdict: Verdict + size?: 'sm' | 'md' +} + +export default function VerdictBadge({ verdict, size = 'sm' }: Props) { + return ( + + {verdict} + + ) +} diff --git a/frontend/lib/api.ts b/frontend/lib/api.ts new file mode 100644 index 0000000000000000000000000000000000000000..f5d3f8164eb17170ca9ecf6ccc4bc12412643072 --- /dev/null +++ b/frontend/lib/api.ts @@ -0,0 +1,33 @@ +// frontend/lib/api.ts + +import type { DetectionResponse } from '@/types/detection' + +const BASE_URL = + process.env.NEXT_PUBLIC_API_URL ?? + 'http://localhost:8000' + +async function _post(endpoint: string, file: File): Promise { + const form = new FormData() + form.append('file', file) + + const res = await fetch(`${BASE_URL}${endpoint}`, { + method: 'POST', + body: form, + }) + + if (!res.ok) { + const err = await res.text() + throw new Error(`Detection failed (${res.status}): ${err}`) + } + + return res.json() +} + +export const detectImage = (file: File) => _post('/detect/image', file) +export const detectVideo = (file: File) => _post('/detect/video', file) + +export async function healthCheck(): Promise<{ status: string; version?: string }> { + const res = await fetch(`${BASE_URL}/health`) + if (!res.ok) throw new Error(`Health check failed: ${res.status}`) + return res.json() +} diff --git a/frontend/lib/constants.ts b/frontend/lib/constants.ts new file mode 100644 index 0000000000000000000000000000000000000000..027cdddcd4a964f253b6c21bf9862826af5f3847 --- /dev/null +++ b/frontend/lib/constants.ts @@ -0,0 +1,46 @@ +// frontend/lib/constants.ts + +import type { GeneratorLabel } from '@/types/detection' + +export const MAX_IMAGE_MB = 20 +export const MAX_VIDEO_MB = 500 + +export const ALLOWED_IMAGE_TYPES = ['image/jpeg', 'image/png', 'image/webp'] +export const ALLOWED_VIDEO_TYPES = ['video/mp4', 'video/quicktime', 'video/x-msvideo'] + +export const IMAGE_FORMAT_LABEL = 'JPG - PNG - WEBP - MAX 20MB' +export const VIDEO_FORMAT_LABEL = 'MP4 - MOV - AVI - MAX 500MB' + +export const GENERATOR_LABELS: Record = { + real: 'Real', + unknown_gan: 'Unknown GAN', + stable_diffusion: 'Stable Diffusion', + midjourney: 'Midjourney', + dall_e: 'DALL-E', + flux: 'FLUX', + firefly: 'Adobe Firefly', + imagen: 'Google Imagen', + sora: 'OpenAI Sora', +} + +export const ENGINE_LABELS: Record = { + fingerprint: 'FINGERPRINT', + coherence: 'COHERENCE', + sstgnn: 'SSTGNN', +} + +export const ORBITAL_NODES = [ + { label: 'Image', icon: 'Image' }, + { label: 'Video', icon: 'Film' }, + { label: 'Audio', icon: 'Mic' }, + { label: 'Face Mesh', icon: 'Scan' }, + { label: 'Generator ID', icon: 'Cpu' }, + { label: 'Confidence', icon: 'BarChart2' }, + { label: 'Report', icon: 'FileText' }, +] as const + +export const LOADING_STAGES = [ + 'FINGERPRINT SCAN', + 'COHERENCE CHECK', + 'GRAPH ANALYSIS', +] as const diff --git a/frontend/lib/utils.ts b/frontend/lib/utils.ts new file mode 100644 index 0000000000000000000000000000000000000000..a1a8e6aada2d0227a79058349882a8bc2ec7eb31 --- /dev/null +++ b/frontend/lib/utils.ts @@ -0,0 +1,29 @@ +// frontend/lib/utils.ts + +import { type ClassValue, clsx } from 'clsx' +import { twMerge } from 'tailwind-merge' + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)) +} + +export function formatBytes(bytes: number): string { + if (bytes === 0) return '0 B' + const k = 1024 + const sizes = ['B', 'KB', 'MB', 'GB'] + const i = Math.floor(Math.log(bytes) / Math.log(k)) + return `${parseFloat((bytes / Math.pow(k, i)).toFixed(1))} ${sizes[i]}` +} + +export function formatMs(ms: number): string { + if (ms < 1000) return `${Math.round(ms)}ms` + return `${(ms / 1000).toFixed(1)}s` +} + +export function formatConfidence(confidence: number): string { + return `${(confidence * 100).toFixed(1)}%` +} + +export function clampConfidence(v: number): number { + return Math.max(0, Math.min(1, v)) +} diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts new file mode 100644 index 0000000000000000000000000000000000000000..254b73c165d9020f2076dc5a74c24bc346e26a2b --- /dev/null +++ b/frontend/next-env.d.ts @@ -0,0 +1,6 @@ +/// +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/pages/api-reference/config/typescript for more information. diff --git a/frontend/next.config.js b/frontend/next.config.js new file mode 100644 index 0000000000000000000000000000000000000000..ce18584e5048b7a6dd8d5d5c99ade7529b3cf0a1 --- /dev/null +++ b/frontend/next.config.js @@ -0,0 +1,17 @@ +const path = require('path') + +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + outputFileTracingRoot: path.join(__dirname), + async rewrites() { + return [ + { + source: '/api/:path*', + destination: `${process.env.NEXT_PUBLIC_API_URL ?? 'http://localhost:8000'}/:path*`, + }, + ] + }, +} + +module.exports = nextConfig diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..fe187b7506c0fb0670cd7fa410632440c56ab2ea --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,14873 @@ +{ + "name": "genai-deepdetect-ui", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "genai-deepdetect-ui", + "version": "0.1.0", + "dependencies": { + "class-variance-authority": "^0.7.0", + "clsx": "^2.1.0", + "framer-motion": "^12.38.0", + "lucide-react": "^0.323.0", + "next": "^15.5.14", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "tailwind-merge": "^2.2.1", + "update": "^0.7.4" + }, + "devDependencies": { + "@types/node": "^20.11.5", + "@types/react": "^18.2.48", + "@types/react-dom": "^18.2.18", + "autoprefixer": "^10.4.17", + "eslint": "^8.56.0", + "eslint-config-next": "^15.5.14", + "postcss": 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b/frontend/package.json @@ -0,0 +1,34 @@ +{ + "name": "genai-deepdetect-ui", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "next lint", + "type-check": "tsc --noEmit" + }, + "dependencies": { + "class-variance-authority": "^0.7.0", + "clsx": "^2.1.0", + "framer-motion": "^12.38.0", + "lucide-react": "^0.323.0", + "next": "^15.5.14", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "tailwind-merge": "^2.2.1", + "update": "^0.7.4" + }, + "devDependencies": { + "@types/node": "^20.11.5", + "@types/react": "^18.2.48", + "@types/react-dom": "^18.2.18", + "autoprefixer": "^10.4.17", + "eslint": "^8.56.0", + "eslint-config-next": "^15.5.14", + "postcss": "^8.4.33", + "tailwindcss": "^3.4.1", + "typescript": "^5.3.3" + } +} diff --git a/frontend/pages/_app.tsx b/frontend/pages/_app.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c2a0424e615c2ee8aef1d6080de49412637a81e5 --- /dev/null +++ b/frontend/pages/_app.tsx @@ -0,0 +1,18 @@ +import type { AppProps } from 'next/app' +import Head from 'next/head' +import '@/styles/globals.css' + +export default function App({ Component, pageProps }: AppProps) { + return ( + <> + + + + + + + + + + ) +} diff --git a/frontend/pages/_document.tsx b/frontend/pages/_document.tsx new file mode 100644 index 0000000000000000000000000000000000000000..10a60569d2fb89a876d86b1cbaa088d0a6a7ce05 --- /dev/null +++ b/frontend/pages/_document.tsx @@ -0,0 +1,20 @@ +import { Html, Head, Main, NextScript } from 'next/document' + +export default function Document() { + return ( + + + + + + + +
+ + + + ) +} diff --git a/frontend/pages/analyze.tsx b/frontend/pages/analyze.tsx new file mode 100644 index 0000000000000000000000000000000000000000..f8250df3c833692c974681a59ff522e86ba8852e --- /dev/null +++ b/frontend/pages/analyze.tsx @@ -0,0 +1,249 @@ +import type { NextPage } from 'next' +import Head from 'next/head' +import { useRouter } from 'next/router' +import { useState, useCallback, useRef } from 'react' +import { motion, AnimatePresence } from 'framer-motion' +import { ArrowLeft, Image as ImageIcon, Film } from 'lucide-react' +import type { AnalysisState, DetectionResponse } from '@/types/detection' +import { detectImage, detectVideo } from '@/lib/api' +import { formatBytes } from '@/lib/utils' +import UploadZone from '@/components/ui/upload-zone' +import LoadingOrbit from '@/components/ui/loading-orbit' +import VerdictBadge from '@/components/ui/verdict-badge' +import EngineBreakdown from '@/components/ui/engine-breakdown' +import MediaMetaStrip from '@/components/ui/media-meta-strip' +import ConfidenceRing from '@/components/ui/confidence-ring' + +const FADE_UP = { + initial: { opacity: 0, y: 14 }, + animate: { opacity: 1, y: 0 }, + transition: { duration: 0.4, ease: [0.25, 0.46, 0.45, 0.94] }, +} + +export default function Analyze(): JSX.Element { + const router = useRouter() + const defaultType = router.query.type === 'video' ? 'video' : 'image' + const [mediaType, setMediaType] = useState<'image' | 'video'>(defaultType) + const [state, setState] = useState({ status: 'idle' }) + + const handleFile = useCallback(async (file: File) => { + const preview = URL.createObjectURL(file) + setState({ status: 'uploading', file, preview }) + + try { + setState(s => ({ ...s, status: 'analysing' })) + const result = mediaType === 'image' + ? await detectImage(file) + : await detectVideo(file) + setState({ status: 'done', file, preview, result }) + } catch (err) { + setState(s => ({ + ...s, + status: 'error', + error: err instanceof Error ? err.message : 'Unknown error', + })) + } + }, [mediaType]) + + const reset = () => { + if (state.preview) URL.revokeObjectURL(state.preview) + setState({ status: 'idle' }) + } + + return ( + <> + + Analyse · GenAI-DeepDetect + + +
+ {/* Nav */} + + +
+ + {/* IDLE — upload zone */} + {state.status === 'idle' && ( + + + {/* Info panel */} +
+ {['FINGERPRINT', 'COHERENCE', 'SSTGNN'].map((engine, i) => ( + +
+ {engine} +
+
+ {engine === 'FINGERPRINT' && 'Frequency-domain artifact analysis via ViT-B/16'} + {engine === 'COHERENCE' && 'Lip-audio synchronisation scoring (video only)'} + {engine === 'SSTGNN' && 'Facial landmark trajectory graph attention (video only)'} +
+ + ))} +
+ + )} + + {/* UPLOADING / ANALYSING — loading orbit */} + {(state.status === 'uploading' || state.status === 'analysing') && ( + + )} + + {/* ERROR */} + {state.status === 'error' && ( + +
+ Error +
+

{state.error}

+ + + )} + + {/* DONE — results */} + {state.status === 'done' && state.result && ( +
+ {/* Header row */} + +
Results
+ + + + {/* Preview + stats */} + + {/* Preview */} +
+ {state.preview && ( + Uploaded media + )} + {!state.preview && ( + + PREVIEW + + )} +
+ +
+
+ + {/* Stats */} +
+ + + + {state.file && ( + + )} +
+ + + {/* Engine breakdown */} + +
Engine breakdown
+ + + + {/* Explanation */} + +
+ Analysis detail +
+

+ {state.result.explanation} +

+ +
+ )} +
+
+ + ) +} + +function StatPanel({ + label, value, mono = false, +}: { label: string; value: string; mono?: boolean }) { + return ( +
+
+ {label} +
+
+ {value} +
+
+ ) +} diff --git a/frontend/pages/index.tsx b/frontend/pages/index.tsx new file mode 100644 index 0000000000000000000000000000000000000000..419ce23b23d84f3cfb37cefd37293303e3b18376 --- /dev/null +++ b/frontend/pages/index.tsx @@ -0,0 +1,206 @@ +import type { NextPage } from 'next'; +import Head from 'next/head'; +import { useRouter } from 'next/router'; +import { motion } from 'framer-motion'; +import { + Image as ImageIcon, + Film, + Mic, + Scan, + Cpu, + BarChart2, + FileText, +} from 'lucide-react'; +import type { LucideIcon } from 'lucide-react'; +import { ORBITAL_NODES } from '@/lib/constants'; + +const ICONS: Record = { + Image: ImageIcon, + Film, + Mic, + Scan, + Cpu, + BarChart2, + FileText, +}; + +// Node positions at radius=140 — 7 nodes, 360/7 ≈ 51.4° apart, starting at top (90°) +function nodePosition(index: number, radius = 140) { + const angle = Math.PI / 2 - (index * 2 * Math.PI) / 7; + return { + x: Math.round(radius * Math.cos(angle)), + y: Math.round(-radius * Math.sin(angle)), // flip Y for CSS + }; +} + +export default function Landing(): JSX.Element { + const router = useRouter(); + + return ( + <> + + GenAI-DeepDetect — Forensic Detection System + + +
+ {/* Nav */} + + + {/* Hero */} +
+ {/* Background paths */} +
+ + + + + + + + +
+ + {/* Eyebrow */} + + Forensic Detection System + + + {/* Orbital diagram */} + + {/* Orbit rings */} + {[280, 200].map((size, i) => ( +
+ ))} + + {/* Center node */} +
+ + GENAI +
+ DETECT + +
+ + {/* Orbital nodes */} + {ORBITAL_NODES.map((node, i) => { + const pos = nodePosition(i); + const Icon = ICONS[node.icon]; + return ( +
+
+ {Icon && ( + + )} +
+ + {node.label} + +
+ ); + })} + + + {/* Headline */} + + Detect AI-Generated +
+ Content + + + + Three independent engines analyse your media for + synthetic fingerprints, temporal inconsistencies, and + facial landmark anomalies. + + + {/* CTAs */} + + + + +
+ + {/* Footer */} +
+ + GENAI-DEEPDETECT · v1.0.0 + + + Fingerprint · Coherence · SSTGNN + +
+
+ + ); +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..33ad091d26d8a9dc95ebdf616e217d985ec215b8 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/styles/globals.css b/frontend/styles/globals.css new file mode 100644 index 0000000000000000000000000000000000000000..79175681f042071be70596b044251271176cf83b --- /dev/null +++ b/frontend/styles/globals.css @@ -0,0 +1,83 @@ +@import url('https://fonts.googleapis.com/css2?family=DM+Mono:ital,wght@0,300;0,400;0,500;1,300&family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;0,9..40,600;1,9..40,300&display=swap'); + +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + --c-bg: #0D0D0D; + --c-surface: #262626; + --c-border: #1A1A1A; + --c-border-hi: #2A2A2A; + --c-muted: #595958; + --c-secondary: #A6A6A6; + --c-primary: #F2F2F2; + --font-sans: 'DM Sans', sans-serif; + --font-mono: 'DM Mono', monospace; + + /* Semantic verdict colors */ + --c-fake-bg: rgba(239, 68, 68, 0.15); + --c-fake-bd: rgba(239, 68, 68, 0.30); + --c-fake-tx: #fca5a5; + --c-real-bg: rgba(34, 197, 94, 0.10); + --c-real-bd: rgba(34, 197, 94, 0.25); + --c-real-tx: #86efac; +} + +* { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +html, +body { + background: var(--c-bg); + color: var(--c-primary); + font-family: var(--font-sans); + font-size: 15px; + line-height: 1.7; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +/* Scrollbar */ +::-webkit-scrollbar { width: 4px; } +::-webkit-scrollbar-track { background: var(--c-bg); } +::-webkit-scrollbar-thumb { background: var(--c-border-hi); border-radius: 2px; } + +/* Focus visible */ +:focus-visible { + outline: 1px solid var(--c-muted); + outline-offset: 2px; +} + +/* Mono utility */ +.font-mono { font-family: var(--font-mono); } + +/* Section label with trailing line */ +.section-label { + display: flex; + align-items: center; + gap: 12px; + font-family: var(--font-mono); + font-size: 10px; + letter-spacing: 0.2em; + text-transform: uppercase; + color: var(--c-muted); +} +.section-label::after { + content: ''; + flex: 1; + height: 1px; + background: #222; +} + +/* Card hover lift */ +.card-interactive { + transition: transform 200ms ease, border-color 200ms ease; +} +.card-interactive:hover { + transform: translateY(-1px); + border-color: rgba(255, 255, 255, 0.15); +} diff --git a/frontend/tailwind.config.ts b/frontend/tailwind.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..861cf3433b66dc94f16f2c273a9f61aa5ce32efe --- /dev/null +++ b/frontend/tailwind.config.ts @@ -0,0 +1,51 @@ +import type { Config } from 'tailwindcss' + +const config: Config = { + content: [ + './pages/**/*.{js,ts,jsx,tsx,mdx}', + './components/**/*.{js,ts,jsx,tsx,mdx}', + ], + theme: { + extend: { + colors: { + brand: { + bg: '#0D0D0D', + surface: '#262626', + border: '#1A1A1A', + borderHi: '#2A2A2A', + muted: '#595958', + secondary: '#A6A6A6', + primary: '#F2F2F2', + }, + }, + fontFamily: { + sans: ['DM Sans', 'sans-serif'], + mono: ['DM Mono', 'monospace'], + }, + borderRadius: { + card: '14px', + panel: '8px', + zone: '10px', + badge: '20px', + }, + animation: { + 'spin-slow': 'spin 2s linear infinite', + 'fade-up': 'fadeUp 0.4s ease-out forwards', + 'pulse-ring':'pulseRing 2s ease-in-out infinite', + }, + keyframes: { + fadeUp: { + '0%': { opacity: '0', transform: 'translateY(12px)' }, + '100%': { opacity: '1', transform: 'translateY(0)' }, + }, + pulseRing: { + '0%, 100%': { opacity: '0.4', transform: 'scale(1)' }, + '50%': { opacity: '0.8', transform: 'scale(1.04)' }, + }, + }, + }, + }, + plugins: [], +} + +export default config diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..ed4fe5e6436d5ed853df90e1bd58173970ee6ce3 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "target": "es5", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "paths": { + "@/components/*": ["./components/*"], + "@/lib/*": ["./lib/*"], + "@/styles/*": ["./styles/*"], + "@/types/*": ["./types/*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"], + "exclude": ["node_modules"] +} diff --git a/frontend/types/detection.ts b/frontend/types/detection.ts new file mode 100644 index 0000000000000000000000000000000000000000..8acdc4f23e2d77d9c3c012ba78484b87f46afa9f --- /dev/null +++ b/frontend/types/detection.ts @@ -0,0 +1,43 @@ +// frontend/types/detection.ts +// Types aligned with src/types.py. + +export type Verdict = 'FAKE' | 'REAL' | 'UNKNOWN' + +export type GeneratorLabel = + | 'real' + | 'unknown_gan' + | 'stable_diffusion' + | 'midjourney' + | 'dall_e' + | 'flux' + | 'firefly' + | 'imagen' + | 'sora' + +export interface EngineResult { + engine: string + verdict: Verdict + confidence: number + attributed_generator?: GeneratorLabel | string | null + explanation: string + processing_time_ms: number +} + +export interface DetectionResponse { + verdict: Verdict + confidence: number + attributed_generator: GeneratorLabel | string + explanation: string + processing_time_ms: number + engine_breakdown: EngineResult[] +} + +export type MediaType = 'image' | 'video' + +export interface AnalysisState { + status: 'idle' | 'uploading' | 'analysing' | 'done' | 'error' + file?: File + preview?: string + result?: DetectionResponse + error?: string +} diff --git a/frontend/types/framer-motion.d.ts b/frontend/types/framer-motion.d.ts new file mode 100644 index 0000000000000000000000000000000000000000..dfbccd5c55e371970a7ee9ff6def270a22535f17 --- /dev/null +++ b/frontend/types/framer-motion.d.ts @@ -0,0 +1 @@ +declare module 'framer-motion'; diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..fb3f9f32757d7e66df555840ad3e2ed40bd9da29 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,1268 @@ +{ + "name": "genai-deepdetect", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "@upstash/context7-mcp": "^2.1.4" + } + }, + "node_modules/@hono/node-server": { + "version": "1.19.11", + "resolved": "https://registry.npmjs.org/@hono/node-server/-/node-server-1.19.11.tgz", + "integrity": "sha512-dr8/3zEaB+p0D2n/IUrlPF1HZm586qgJNXK1a9fhg/PzdtkK7Ksd5l312tJX2yBuALqDYBlG20QEbayqPyxn+g==", + "license": "MIT", + "engines": { + "node": ">=18.14.1" + }, + "peerDependencies": { + "hono": "^4" + } 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/dev/null +++ b/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +addopts = -p no:tmpdir -p no:cacheprovider --ignore=tests/.tmp +testpaths = tests +norecursedirs = .tmp tmp* pytest_tmp node_modules frontend tmp_everything_claude_code diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e1a768b5b7be64f2bb33d475fb2e49cb015cd932 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,42 @@ +# API +fastapi>=0.111.0 +uvicorn[standard]>=0.29.0 +python-multipart>=0.0.9 +aiofiles>=23.2.1 +httpx>=0.27.0 +pydantic>=2.7.0 +python-dotenv>=1.0.1 + +# ML - fingerprint +transformers>=4.40.0 +timm>=1.0.0 +torch>=2.1.0 +torchvision>=0.16.0 + +# ML - coherence +# facenet-pytorch currently has limited support on newer Python versions. +facenet-pytorch>=2.5.3; python_version < "3.13" +mediapipe>=0.10.14 +opencv-python-headless>=4.9.0 + +# ML - sstgnn +torch-geometric>=2.5.0 +scipy>=1.13.0 + +# Explainability - Gemini +google-generativeai>=0.8.0 + +# HuggingFace +huggingface-hub>=0.23.0 + +# RunPod serverless handler +runpod>=1.6.0 + +# Continual learning +apscheduler>=3.10.4 + +# Utils +Pillow>=10.3.0 +numpy>=1.26.0; python_version < "3.13" +numpy>=2.0.0; python_version >= "3.13" +scikit-learn>=1.5.0 diff --git a/runpod_handler.py b/runpod_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..cea2a0b29c72a8742e52c6bead8dd8040fdef0ae --- /dev/null +++ b/runpod_handler.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import base64 +import io +import os +import tempfile +import time + +from PIL import Image + +os.environ.setdefault("MODEL_CACHE_DIR", "/tmp/models") +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") + +from src.engines.coherence.engine import CoherenceEngine +from src.engines.fingerprint.engine import FingerprintEngine +from src.engines.sstgnn.engine import SSTGNNEngine +from src.explainability.explainer import explain +from src.fusion.fuser import fuse + +_fp = FingerprintEngine() +_co = CoherenceEngine() +_st = SSTGNNEngine() + + +def _extract_frames(video_path: str) -> list: + try: + import cv2 + except Exception: + return [] + + cap = cv2.VideoCapture(video_path) + frames = [] + index = 0 + while True: + ret, frame = cap.read() + if not ret: + break + if index % 4 == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + index += 1 + if len(frames) >= 300: + break + cap.release() + return frames + + +def handler(job: dict) -> dict: + inp = job.get("input", {}) + encoded = inp.get("data") or inp.get("image_b64") + if not encoded: + raise ValueError("Missing input.data (base64 payload)") + + raw = base64.b64decode(encoded) + media_type = str(inp.get("media_type", "image")).lower() + + t0 = time.perf_counter() + + if media_type == "image": + image = Image.open(io.BytesIO(raw)).convert("RGB") + fp = _fp.run(image) + co = _co.run(image) + st = _st.run(image) + verdict, conf, generator = fuse([fp, co, st], is_video=False) + else: + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp: + temp.write(raw) + tmp_path = temp.name + + try: + frames = _extract_frames(tmp_path) + finally: + os.unlink(tmp_path) + + fp = _fp.run_video(frames) + co = _co.run_video(frames) + st = _st.run_video(frames) + verdict, conf, generator = fuse([fp, co, st], is_video=True) + + engine_results = [fp, co, st] + explanation = explain(verdict, conf, engine_results, generator) + total_ms = (time.perf_counter() - t0) * 1000 + + return { + "verdict": verdict, + "confidence": conf, + "attributed_generator": generator, + "explanation": explanation, + "processing_time_ms": total_ms, + "engine_breakdown": [result.model_dump() for result in engine_results], + } + + +try: + import runpod # type: ignore +except Exception: + runpod = None + + +if runpod is not None: + runpod.serverless.start({"handler": handler}) diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/scripts/assemble_image_baseline.py b/scripts/assemble_image_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..2f68cfbf9c5947ee7c378d1b6b6c03115c91477a --- /dev/null +++ b/scripts/assemble_image_baseline.py @@ -0,0 +1,37 @@ +""" +scripts/assemble_image_baseline.py + +Assembles the image baseline dataset manifest from multiple prepared source +directories. Delegates to build_dataset.py with the correct Kaggle input paths. + +Kaggle usage: + !python scripts/assemble_image_baseline.py \ + --faces_140k /kaggle/input/140k-real-and-fake-faces \ + --ai_vs_real /kaggle/input/ai-generated-vs-real-images-datasaet \ + --output_dir /kaggle/working/processed/fingerprint +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent.parent)) +from training.phase1_fingerprint.build_dataset import build, parse_args as _parse + + +def main() -> None: + p = argparse.ArgumentParser(description="Assemble image baseline dataset (Kaggle)") + p.add_argument("--faces_140k", default=None) + p.add_argument("--ai_vs_real", default=None) + p.add_argument("--deepfake_real", default=None) + p.add_argument("--deepfake_faces",default=None) + p.add_argument("--celebdf", default=None) + p.add_argument("--output_dir", default="/kaggle/working/processed/fingerprint") + p.add_argument("--seed", type=int, default=42) + args = p.parse_args() + build(args) + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_kaggle_dataset.py b/scripts/bootstrap_kaggle_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..9f57979db0d240fea492df118aa9fdd1929cbee6 --- /dev/null +++ b/scripts/bootstrap_kaggle_dataset.py @@ -0,0 +1,72 @@ +""" +scripts/bootstrap_kaggle_dataset.py + +Uploads processed dataset to Kaggle as a private dataset so it can be +reused across notebook sessions without re-running preprocessing. + +Usage (run locally after downloading processed data from Kaggle): + python scripts/bootstrap_kaggle_dataset.py \ + --processed_dir /path/to/processed/fingerprint \ + --dataset_name deepdetect-fingerprint-processed \ + --title "DeepDetect Fingerprint Processed Dataset" +""" +from __future__ import annotations + +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path + + +def main(args: argparse.Namespace) -> None: + processed_dir = Path(args.processed_dir) + dataset_name = args.dataset_name + kaggle_username = args.username or os.environ.get("KAGGLE_USERNAME") + + if not kaggle_username: + print("ERROR: Kaggle username required. Set --username or KAGGLE_USERNAME env var.") + sys.exit(1) + + if not processed_dir.exists(): + print(f"ERROR: Directory not found: {processed_dir}") + sys.exit(1) + + # Write dataset-metadata.json + meta = { + "title": args.title, + "id": f"{kaggle_username}/{dataset_name}", + "licenses": [{"name": "CC0-1.0"}], + } + meta_path = processed_dir / "dataset-metadata.json" + with open(meta_path, "w") as f: + json.dump(meta, f, indent=2) + + print(f"Creating Kaggle dataset: {kaggle_username}/{dataset_name}") + result = subprocess.run( + ["kaggle", "datasets", "create", "-p", str(processed_dir), "--dir-mode", "zip"], + capture_output=True, text=True, + ) + + if result.returncode == 0: + print(f"Dataset created: https://www.kaggle.com/datasets/{kaggle_username}/{dataset_name}") + else: + print(f"STDOUT: {result.stdout}") + print(f"STDERR: {result.stderr}") + print("If dataset already exists, use 'kaggle datasets version' to update it.") + + meta_path.unlink() # clean up + + +def parse_args(): + p = argparse.ArgumentParser(description="Upload processed dataset to Kaggle") + p.add_argument("--processed_dir", required=True) + p.add_argument("--dataset_name", required=True) + p.add_argument("--title", default="DeepDetect Processed Dataset") + p.add_argument("--username", default=None) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/demo_smoke_test.py b/scripts/demo_smoke_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ef4e542004f45c81931e1528e7e1c13aec8f4dc3 --- /dev/null +++ b/scripts/demo_smoke_test.py @@ -0,0 +1,298 @@ +""" +scripts/demo_smoke_test.py + +Prepare demo sample media and run one-command smoke tests against the running API. + +Usage: + python scripts/demo_smoke_test.py + python scripts/demo_smoke_test.py --base-url http://localhost:8000 + +Environment: + DEMO_API_BASE_URL overrides default base URL. +""" + +from __future__ import annotations + +import argparse +import logging +import os +import sys +from pathlib import Path +from typing import Any, cast + +import httpx +import numpy as np +from PIL import Image, ImageDraw + + +LOG_FORMAT = "%(levelname)s: %(message)s" +logging.basicConfig(level=logging.INFO, format=LOG_FORMAT) +log = logging.getLogger(__name__) + + +SAMPLE_DIR = Path("data/samples") +REQUIRED_RESPONSE_KEYS = { + "verdict", + "confidence", + "attributed_generator", + "explanation", + "processing_time_ms", + "engine_breakdown", +} + + +def _download_file(url: str, output: Path) -> bool: + try: + with httpx.Client(timeout=20.0, follow_redirects=True) as client: + resp = client.get(url) + resp.raise_for_status() + output.write_bytes(resp.content) + return True + except Exception as exc: + log.warning("Could not download %s (%s)", url, exc) + return False + + +def _make_fallback_fake_image(fake_img: Path) -> None: + # Synthetic-looking sample (deterministic). + fake = Image.new("RGB", (512, 512), (12, 12, 12)) + draw_fake = ImageDraw.Draw(fake) + for i in range(0, 512, 16): + color = (40 + (i % 120), 40, 120 + (i % 80)) + draw_fake.rectangle([i, 0, i + 8, 511], fill=color) + draw_fake.ellipse([140, 110, 380, 350], outline=(220, 220, 220), width=4) + draw_fake.text((168, 380), "SYNTHETIC SAMPLE", fill=(220, 220, 220)) + fake.save(fake_img, format="JPEG", quality=95) + + +def _make_fallback_real_image(real_img: Path) -> None: + # Natural-looking simple fallback when a real photo cannot be downloaded. + arr = np.zeros((512, 512, 3), dtype=np.uint8) + for y in range(512): + arr[y, :, 0] = np.uint8(200 - y // 4) + arr[y, :, 1] = np.uint8(160 - y // 6) + arr[y, :, 2] = np.uint8(140 - y // 8) + real = Image.fromarray(arr, mode="RGB") + draw_real = ImageDraw.Draw(real) + draw_real.rectangle([56, 280, 456, 508], outline=(245, 245, 245), width=2) + draw_real.text((70, 468), "REAL-LIKE SAMPLE", fill=(245, 245, 245)) + real.save(real_img, format="JPEG", quality=95) + + +def _prepare_images( + sample_dir: Path, + refresh: bool = False, + use_web_samples: bool = False, +) -> tuple[Path, Path]: + sample_dir.mkdir(parents=True, exist_ok=True) + fake_img = sample_dir / "fake_1.jpg" + real_img = sample_dir / "real_1.jpg" + + if not refresh and fake_img.exists() and real_img.exists(): + return fake_img, real_img + + fake_ok = False + if use_web_samples: + fake_ok = _download_file("https://thispersondoesnotexist.com", fake_img) + if not fake_ok: + _make_fallback_fake_image(fake_img) + + real_ok = _download_file( + "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?w=512", + real_img, + ) + if not real_ok: + log.info("Could not download real sample image; using generated fallback real image.") + _make_fallback_real_image(real_img) + + return fake_img, real_img + + +def _prepare_video(sample_dir: Path) -> Path: + import cv2 + + sample_dir.mkdir(parents=True, exist_ok=True) + video_path = sample_dir / "demo_1.avi" + if video_path.exists() and video_path.stat().st_size > 0: + return video_path + + width, height = 512, 512 + fps = 10.0 + frames = 40 + + fourcc_fn = cast(Any, getattr(cv2, "VideoWriter_fourcc", None)) + if not callable(fourcc_fn): + raise RuntimeError("OpenCV VideoWriter_fourcc is unavailable") + fourcc_xvid = cast(int, fourcc_fn(*"XVID")) + fourcc_mjpg = cast(int, fourcc_fn(*"MJPG")) + + writer = cv2.VideoWriter( + str(video_path), fourcc_xvid, fps, (width, height) + ) + if not writer.isOpened(): + writer = cv2.VideoWriter( + str(video_path), fourcc_mjpg, fps, (width, height) + ) + + if not writer.isOpened(): + raise RuntimeError("Could not initialize AVI video writer") + + for i in range(frames): + frame = np.zeros((height, width, 3), dtype=np.uint8) + # Background gradient animation + frame[:, :, 0] = np.uint8((40 + i * 3) % 255) + frame[:, :, 1] = np.uint8((20 + i * 2) % 255) + frame[:, :, 2] = np.uint8((60 + i * 4) % 255) + + # Moving face-like circle + eyes/mouth + cx = int(120 + (i * 8) % 280) + cy = 220 + cv2.circle(frame, (cx, cy), 70, (220, 220, 220), 2) + cv2.circle(frame, (cx - 24, cy - 12), 6, (230, 230, 230), -1) + cv2.circle(frame, (cx + 24, cy - 12), 6, (230, 230, 230), -1) + cv2.ellipse(frame, (cx, cy + 20), (24, 12), 0, 0, 180, (230, 230, 230), 2) + + cv2.putText( + frame, + "DEMO VIDEO SAMPLE", + (110, 470), + cv2.FONT_HERSHEY_SIMPLEX, + 0.8, + (245, 245, 245), + 2, + cv2.LINE_AA, + ) + writer.write(frame) + + writer.release() + + if not video_path.exists() or video_path.stat().st_size == 0: + raise RuntimeError("Video file was not written") + + return video_path + + +def _validate_response(payload: dict) -> None: + missing = REQUIRED_RESPONSE_KEYS - set(payload.keys()) + if missing: + raise ValueError(f"Response missing keys: {sorted(missing)}") + + verdict = payload.get("verdict") + confidence = payload.get("confidence") + + if verdict not in {"FAKE", "REAL"}: + raise ValueError(f"Invalid verdict: {verdict}") + if not isinstance(confidence, (int, float)): + raise ValueError("confidence is not numeric") + if confidence < 0.0 or confidence > 1.0: + raise ValueError(f"confidence out of range: {confidence}") + + +def _post_file(base_url: str, endpoint: str, path: Path, content_type: str) -> dict: + with httpx.Client(timeout=60.0) as client: + with path.open("rb") as fh: + files = {"file": (path.name, fh, content_type)} + resp = client.post(f"{base_url}{endpoint}", files=files) + + if resp.status_code != 200: + raise RuntimeError( + f"{endpoint} returned {resp.status_code}: {resp.text[:300]}" + ) + + payload = resp.json() + _validate_response(payload) + return payload + + +def _check_health(base_url: str) -> None: + with httpx.Client(timeout=15.0) as client: + resp = client.get(f"{base_url}/health") + resp.raise_for_status() + + +def run_smoke( + base_url: str, + refresh_samples: bool = False, + use_web_samples: bool = False, +) -> int: + base_url = base_url.rstrip("/") + log.info("Using API base URL: %s", base_url) + + _check_health(base_url) + log.info("Health check OK") + + fake_img, real_img = _prepare_images( + SAMPLE_DIR, + refresh=refresh_samples, + use_web_samples=use_web_samples, + ) + video = _prepare_video(SAMPLE_DIR) + + image_fake = _post_file(base_url, "/detect/image", fake_img, "image/jpeg") + image_real = _post_file(base_url, "/detect/image", real_img, "image/jpeg") + video_res = _post_file(base_url, "/detect/video", video, "video/x-msvideo") + + log.info( + "Image test 1 (%s): verdict=%s confidence=%.3f generator=%s", + fake_img.name, + image_fake["verdict"], + float(image_fake["confidence"]), + image_fake["attributed_generator"], + ) + log.info( + "Image test 2 (%s): verdict=%s confidence=%.3f generator=%s", + real_img.name, + image_real["verdict"], + float(image_real["confidence"]), + image_real["attributed_generator"], + ) + log.info( + "Video test (%s): verdict=%s confidence=%.3f generator=%s", + video.name, + video_res["verdict"], + float(video_res["confidence"]), + video_res["attributed_generator"], + ) + + log.info("Smoke test PASSED") + return 0 + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Prepare demo media and smoke-test image/video endpoints" + ) + parser.add_argument( + "--base-url", + default=os.environ.get("DEMO_API_BASE_URL", "http://localhost:8000"), + help="API base URL (default: %(default)s)", + ) + parser.add_argument( + "--refresh-samples", + action="store_true", + help="Re-download/regenerate demo samples instead of reusing existing files", + ) + parser.add_argument( + "--use-web-samples", + action="store_true", + help="Use downloaded web sample for the fake image (less deterministic)", + ) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + base_url = args.base_url + try: + return run_smoke( + base_url, + refresh_samples=args.refresh_samples, + use_web_samples=args.use_web_samples, + ) + except Exception as exc: + log.error("Smoke test FAILED: %s", exc) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/kaggle_train_image_baseline.py b/scripts/kaggle_train_image_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..b2480b72a72e3782bb70f1472b92771468344d97 --- /dev/null +++ b/scripts/kaggle_train_image_baseline.py @@ -0,0 +1,235 @@ +""" +scripts/kaggle_train_image_baseline.py + +Self-contained Kaggle training script. +Paste directly into a Kaggle notebook code cell and run. +No separate data prep step needed — builds the dataset inline from Kaggle input mounts. + +Datasets needed (add via Notebook → Add Data): + - xhlulu/140k-real-and-fake-faces + - philosopher0/ai-generated-vs-real-images-datasaet + +Accelerator: P100 (16GB VRAM) +""" +from __future__ import annotations + +import os, sys, logging, random, time, csv, shutil, hashlib +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import GradScaler, autocast +from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler +from torchvision import transforms +from PIL import Image +from sklearn.metrics import roc_auc_score, accuracy_score +from tqdm import tqdm + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +# ── Configuration — edit these ──────────────────────────────────────────────── + +CFG = { + "faces_140k": "/kaggle/input/140k-real-and-fake-faces", + "ai_vs_real": "/kaggle/input/ai-generated-vs-real-images-datasaet", + "processed": "/kaggle/working/processed/fingerprint", + "output": "/kaggle/working/checkpoints/fingerprint", + "epochs": 5, # 5 for demo (~55 min), 30 for full run (~6h) + "batch_size": 64, + "lr": 2e-5, + "amp": True, + "seed": 42, + "workers": 2, +} + +torch.manual_seed(CFG["seed"]) +np.random.seed(CFG["seed"]) +random.seed(CFG["seed"]) + +Path(CFG["processed"]).mkdir(parents=True, exist_ok=True) +Path(CFG["output"]).mkdir(parents=True, exist_ok=True) + +# ── GPU check ──────────────────────────────────────────────────────────────── + +print(f"PyTorch {torch.__version__} | CUDA {torch.cuda.is_available()}") +if torch.cuda.is_available(): + print(f"GPU: {torch.cuda.get_device_name(0)}") +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# ── Dataset build ───────────────────────────────────────────────────────────── + +IMG_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + +def collect_images(root, label_str): + records = [] + for p in Path(root).rglob("*"): + if p.suffix.lower() in IMG_EXTS: + records.append((p, label_str)) + return records + +def build_dataset(): + log.info("Building fingerprint dataset ...") + all_records = [] + + # 140k faces: has train/real and train/fake folders + if Path(CFG["faces_140k"]).exists(): + for split in ["train", "valid", ""]: + for label in ["real", "fake"]: + d = Path(CFG["faces_140k"]) / split / label if split else Path(CFG["faces_140k"]) / label + if d.exists(): + imgs = collect_images(d, label) + all_records.extend(imgs) + log.info(f" 140k/{split or 'root'}/{label}: {len(imgs)}") + + # AI vs real: subfolder name = generator + if Path(CFG["ai_vs_real"]).exists(): + for sub in Path(CFG["ai_vs_real"]).iterdir(): + if not sub.is_dir(): continue + label = "real" if "real" in sub.name.lower() else "fake" + imgs = collect_images(sub, label) + all_records.extend(imgs) + log.info(f" ai_vs_real/{sub.name} → {label}: {len(imgs)}") + + random.shuffle(all_records) + n = len(all_records) + n_train = int(n * 0.80) + n_val = int(n * 0.10) + splits = { + "train": all_records[:n_train], + "val": all_records[n_train:n_train+n_val], + "test": all_records[n_train+n_val:], + } + + seen = set() + for split, records in splits.items(): + for src, label in tqdm(records, desc=f"Copying {split}"): + h = hashlib.md5(src.read_bytes()).hexdigest()[:8] + dst = Path(CFG["processed"]) / split / label / f"{h}_{src.name}" + if h not in seen and not dst.exists(): + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + seen.add(h) + + for split in ["train", "val"]: + for label in ["real", "fake"]: + count = len(list((Path(CFG["processed"]) / split / label).glob("*"))) + log.info(f" {split}/{label}: {count}") + +build_dataset() + +# ── Transforms ──────────────────────────────────────────────────────────────── + +MEAN, STD = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] +TRAIN_TF = transforms.Compose([ + transforms.RandomResizedCrop(224, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(0.2, 0.2, 0.2, 0.1), + transforms.ToTensor(), + transforms.Normalize(MEAN, STD), +]) +VAL_TF = transforms.Compose([ + transforms.Resize(256), transforms.CenterCrop(224), + transforms.ToTensor(), transforms.Normalize(MEAN, STD), +]) + +# ── Dataset class ────────────────────────────────────────────────────────────── + +class ImgDataset(Dataset): + def __init__(self, root, split, transform): + self.transform = transform + self.samples = [] + for label, is_fake in [("real", 0), ("fake", 1)]: + d = Path(root) / split / label + if not d.exists(): continue + for p in d.iterdir(): + if p.suffix.lower() in IMG_EXTS: + self.samples.append((p, is_fake)) + log.info(f"Dataset [{split}]: {len(self.samples)} images") + + def __len__(self): return len(self.samples) + def __getitem__(self, i): + p, label = self.samples[i] + img = Image.open(p).convert("RGB") + return self.transform(img), label + +train_ds = ImgDataset(CFG["processed"], "train", TRAIN_TF) +val_ds = ImgDataset(CFG["processed"], "val", VAL_TF) +labels = [s[1] for s in train_ds.samples] +n_real = labels.count(0); n_fake = labels.count(1) +weights = [1.0/n_real if l==0 else 1.0/n_fake for l in labels] +sampler = WeightedRandomSampler(weights, len(weights), replacement=True) +train_dl = DataLoader(train_ds, batch_size=CFG["batch_size"], sampler=sampler, + num_workers=CFG["workers"], pin_memory=True) +val_dl = DataLoader(val_ds, batch_size=CFG["batch_size"]*2, shuffle=False, + num_workers=CFG["workers"], pin_memory=True) + +# ── Model ───────────────────────────────────────────────────────────────────── + +import timm +class FingerprintModel(nn.Module): + def __init__(self): + super().__init__() + self.backbone = timm.create_model("vit_base_patch16_224", pretrained=True, + num_classes=0, global_pool="token") + d = self.backbone.num_features + self.binary_head = nn.Sequential(nn.Linear(d, 384), nn.GELU(), nn.Dropout(0.1), nn.Linear(384, 2)) + self.generator_head = nn.Sequential(nn.Linear(d, 384), nn.GELU(), nn.Dropout(0.1), nn.Linear(384, 8)) + + def forward(self, x): + f = self.backbone(x) + return {"binary_logits": self.binary_head(f), "generator_logits": self.generator_head(f)} + +model = FingerprintModel().to(device) +cw = torch.tensor([1.0/n_real, 1.0/n_fake], dtype=torch.float32).to(device) +criterion = nn.CrossEntropyLoss(weight=cw, label_smoothing=0.05) +optimizer = torch.optim.AdamW(model.parameters(), lr=CFG["lr"], weight_decay=0.01) +scaler = GradScaler(enabled=CFG["amp"]) + +# ── Training loop ───────────────────────────────────────────────────────────── + +best_auc = 0.0 +for epoch in range(CFG["epochs"]): + model.train() + t0 = time.time() + for imgs, labels_b in tqdm(train_dl, desc=f"Train E{epoch+1}", leave=False): + imgs, labels_b = imgs.to(device), labels_b.to(device) + with autocast(enabled=CFG["amp"]): + out = model(imgs) + loss = criterion(out["binary_logits"], labels_b) + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + scaler.step(optimizer); scaler.update(); optimizer.zero_grad() + + # Validate + model.eval() + all_probs, all_labels = [], [] + with torch.no_grad(): + for imgs, labels_b in tqdm(val_dl, desc="Val", leave=False): + imgs = imgs.to(device) + out = model(imgs) + probs = torch.softmax(out["binary_logits"], 1)[:, 1].cpu().numpy() + all_probs.extend(probs); all_labels.extend(labels_b.numpy()) + + auc = roc_auc_score(all_labels, all_probs) if len(set(all_labels)) > 1 else 0.5 + acc = accuracy_score(all_labels, (np.array(all_probs) > 0.5).astype(int)) + elapsed = time.time() - t0 + log.info(f"Epoch {epoch+1}/{CFG['epochs']} | val_auc={auc:.4f} | val_acc={acc:.4f} | {elapsed:.0f}s") + + if auc > best_auc: + best_auc = auc + ckpt = { + "epoch": epoch, "model_state": model.state_dict(), + "val_auc": auc, "config": CFG, + } + torch.save(ckpt, Path(CFG["output"]) / "best.pt") + log.info(f" Saved best.pt (AUC={best_auc:.4f})") + + torch.save({"epoch": epoch, "model_state": model.state_dict(), + "val_auc": auc, "config": CFG}, + Path(CFG["output"]) / "latest.pt") + +print(f"\nTraining complete. Best val AUC: {best_auc:.4f}") +print(f"Download: /kaggle/working/checkpoints/fingerprint/best.pt") diff --git a/scripts/prepare_cifake.py b/scripts/prepare_cifake.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bda896caf553a5a3a31520e992763b2a296862 --- /dev/null +++ b/scripts/prepare_cifake.py @@ -0,0 +1,76 @@ +""" +scripts/prepare_cifake.py + +Prepares the CIFAKE dataset (Kaggle: bird-coder/cifake-real-and-ai-generated-synthetic-images) +for use in the fingerprint engine training pipeline. + +CIFAKE contains 60k real images (CIFAR-10) and 60k AI-generated equivalents. +Useful as extra training data for the fingerprint engine. + +Kaggle usage: + !python scripts/prepare_cifake.py \ + --source /kaggle/input/cifake-real-and-ai-generated-synthetic-images \ + --output /kaggle/working/processed/fingerprint \ + --max_per_class 20000 +""" +from __future__ import annotations + +import argparse +import logging +import random +import shutil +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +IMG_EXTS = {".jpg", ".jpeg", ".png"} + + +def main(args: argparse.Namespace) -> None: + source = Path(args.source) + output = Path(args.output) + rng = random.Random(args.seed) + + if not source.exists(): + log.error(f"Source not found: {source}") + return + + for split in ["train", "test"]: + for label, is_fake in [("REAL", "real"), ("FAKE", "fake")]: + src_dir = source / split / label + if not src_dir.exists(): + src_dir = source / label + if not src_dir.exists(): + log.warning(f" Not found: {src_dir}") + continue + + imgs = [p for p in src_dir.iterdir() if p.suffix.lower() in IMG_EXTS] + rng.shuffle(imgs) + imgs = imgs[:args.max_per_class] + + out_split = "train" if split == "train" else "val" + dst_dir = output / out_split / is_fake + dst_dir.mkdir(parents=True, exist_ok=True) + + for img in imgs: + dst = dst_dir / f"cifake_{img.name}" + if not dst.exists(): + shutil.copy2(img, dst) + + log.info(f" cifake/{split}/{label} → {out_split}/{is_fake}: {len(imgs)} images") + + log.info("CIFAKE preparation complete.") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--source", default="/kaggle/input/cifake-real-and-ai-generated-synthetic-images") + p.add_argument("--output", default="/kaggle/working/processed/fingerprint") + p.add_argument("--max_per_class", type=int, default=20000) + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/prepare_cocoxgen.py b/scripts/prepare_cocoxgen.py new file mode 100644 index 0000000000000000000000000000000000000000..64e4b791afc36118f5c9045a64ed41d1557b36b4 --- /dev/null +++ b/scripts/prepare_cocoxgen.py @@ -0,0 +1,76 @@ +""" +scripts/prepare_cocoxgen.py + +Prepares COCO-XGen dataset for fingerprint engine training. +Maps COCO real images against XL-generated equivalents for generator attribution training. + +Kaggle usage: + !python scripts/prepare_cocoxgen.py \ + --real_source /kaggle/input/coco-2017-dataset/coco2017/val2017 \ + --fake_source /kaggle/input/coco-xgen-synthetic \ + --output /kaggle/working/processed/fingerprint \ + --max 15000 +""" +from __future__ import annotations + +import argparse +import logging +import random +import shutil +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +IMG_EXTS = {".jpg", ".jpeg", ".png"} + + +def copy_subset(src_dir: Path, dst_dir: Path, max_n: int, prefix: str, rng: random.Random) -> int: + dst_dir.mkdir(parents=True, exist_ok=True) + imgs = [p for p in src_dir.rglob("*") if p.suffix.lower() in IMG_EXTS] + rng.shuffle(imgs) + imgs = imgs[:max_n] + for img in imgs: + dst = dst_dir / f"{prefix}_{img.name}" + if not dst.exists(): + shutil.copy2(img, dst) + return len(imgs) + + +def main(args: argparse.Namespace) -> None: + rng = random.Random(args.seed) + + splits = {"train": int(args.max * 0.9), "val": int(args.max * 0.1)} + + for split, max_n in splits.items(): + if args.real_source and Path(args.real_source).exists(): + n = copy_subset( + Path(args.real_source), + Path(args.output) / split / "real", + max_n, "coco", rng + ) + log.info(f" real/{split}: {n} images") + + if args.fake_source and Path(args.fake_source).exists(): + n = copy_subset( + Path(args.fake_source), + Path(args.output) / split / "fake", + max_n, "xgen", rng + ) + log.info(f" fake/{split}: {n} images (generator: stable_diffusion/xl)") + + log.info("COCO-XGen preparation complete.") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--real_source", default=None) + p.add_argument("--fake_source", default=None) + p.add_argument("--output", default="/kaggle/working/processed/fingerprint") + p.add_argument("--max", type=int, default=15000) + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/prepare_hf_source.py b/scripts/prepare_hf_source.py new file mode 100644 index 0000000000000000000000000000000000000000..e12df356fe66a1bf722692ea2feeae455f603353 --- /dev/null +++ b/scripts/prepare_hf_source.py @@ -0,0 +1,79 @@ +""" +scripts/prepare_hf_source.py + +Downloads and prepares datasets from HuggingFace Hub for fingerprint engine training. +Requires: pip install datasets huggingface_hub + +Kaggle usage (internet must be enabled in notebook settings): + !pip install datasets huggingface_hub -q + !python scripts/prepare_hf_source.py \ + --dataset elsaEU/ELSA_1M \ + --split train \ + --output /kaggle/working/processed/fingerprint \ + --max 10000 +""" +from __future__ import annotations + +import argparse +import logging +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + + +def main(args: argparse.Namespace) -> None: + try: + from datasets import load_dataset + except ImportError: + log.error("datasets not installed. Run: pip install datasets") + return + + log.info(f"Loading {args.dataset} (split={args.split}, max={args.max}) ...") + + try: + ds = load_dataset(args.dataset, split=args.split, streaming=True) + except Exception as e: + log.error(f"Failed to load dataset: {e}") + return + + output = Path(args.output) + real_dir = output / "train" / "real" + fake_dir = output / "train" / "fake" + real_dir.mkdir(parents=True, exist_ok=True) + fake_dir.mkdir(parents=True, exist_ok=True) + + n_saved = 0 + for i, sample in enumerate(ds): + if n_saved >= args.max: + break + + img = sample.get("image") or sample.get("img") + label = sample.get("label", sample.get("is_fake", 0)) + + if img is None: + continue + + dst_dir = fake_dir if label else real_dir + dst = dst_dir / f"hf_{i:07d}.jpg" + if not dst.exists(): + img.save(dst, format="JPEG", quality=95) + n_saved += 1 + + if n_saved % 1000 == 0: + log.info(f" Saved {n_saved}/{args.max} ...") + + log.info(f"HuggingFace source preparation complete. {n_saved} images saved.") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--dataset", default="elsaEU/ELSA_1M") + p.add_argument("--split", default="train") + p.add_argument("--output", default="/kaggle/working/processed/fingerprint") + p.add_argument("--max", type=int, default=10000) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/prepare_ifakefakedb.py b/scripts/prepare_ifakefakedb.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2ef254539cd7b893a1b25e9f2ee57071bba18d --- /dev/null +++ b/scripts/prepare_ifakefakedb.py @@ -0,0 +1,66 @@ +""" +scripts/prepare_ifakefakedb.py + +Prepares the iFakeFaceDB dataset for fingerprint engine training. +Kaggle slug: tapakah68/artificial-faces-dataset or similar. + +iFakeFaceDB contains ~87k StyleGAN-generated fake faces, useful for +increasing unknown_gan class coverage. + +Kaggle usage: + !python scripts/prepare_ifakefakedb.py \ + --source /kaggle/input/artificial-faces-dataset \ + --output /kaggle/working/processed/fingerprint \ + --max 20000 +""" +from __future__ import annotations + +import argparse +import logging +import random +import shutil +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +IMG_EXTS = {".jpg", ".jpeg", ".png"} + + +def main(args: argparse.Namespace) -> None: + source = Path(args.source) + if not source.exists(): + log.error(f"Source not found: {source}") + return + + rng = random.Random(args.seed) + imgs = [p for p in source.rglob("*") if p.suffix.lower() in IMG_EXTS] + rng.shuffle(imgs) + imgs = imgs[:args.max] + + n_train = int(len(imgs) * 0.85) + splits = {"train": imgs[:n_train], "val": imgs[n_train:]} + + for split, subset in splits.items(): + dst_dir = Path(args.output) / split / "fake" + dst_dir.mkdir(parents=True, exist_ok=True) + for img in subset: + dst = dst_dir / f"ifake_{img.name}" + if not dst.exists(): + shutil.copy2(img, dst) + log.info(f" {split}/fake: {len(subset)} images (generator: unknown_gan / StyleGAN)") + + log.info("iFakeFaceDB preparation complete.") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--source", default="/kaggle/input/artificial-faces-dataset") + p.add_argument("--output", default="/kaggle/working/processed/fingerprint") + p.add_argument("--max", type=int, default=20000) + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/render_kaggle_config.py b/scripts/render_kaggle_config.py new file mode 100644 index 0000000000000000000000000000000000000000..5cb5b6df3b9f719b8c244e57cc5acaeb3ef45d8a --- /dev/null +++ b/scripts/render_kaggle_config.py @@ -0,0 +1,64 @@ +""" +scripts/render_kaggle_config.py + +Generates a ready-to-paste Kaggle notebook JSON config from a YAML training config. +Useful for reproducible Kaggle runs. + +Usage: + python scripts/render_kaggle_config.py --config configs/fingerprint_baseline.yaml +""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import yaml + + +def main(args: argparse.Namespace) -> None: + config_path = Path(args.config) + with open(config_path) as f: + cfg = yaml.safe_load(f) + + engine = cfg.get("engine", "fingerprint") + + notebook_config = { + "id": f"akagtag/deepdetect-{engine}-training", + "language": "python", + "kernel_type": "notebook", + "is_private": True, + "enable_gpu": True, + "enable_tpu": False, + "enable_internet": True, + "dataset_sources": cfg.get("kaggle_datasets", []), + "competition_sources": cfg.get("kaggle_competitions", []), + "kernel_sources": [], + } + + out_path = Path(f".kaggle-kernel/kernel-metadata-{engine}.json") + out_path.parent.mkdir(parents=True, exist_ok=True) + with open(out_path, "w") as f: + json.dump(notebook_config, f, indent=2) + + print(f"Kaggle notebook config written to {out_path}") + print(f"\nTraining args from config:") + train_args = cfg.get("train_args", {}) + for k, v in train_args.items(): + print(f" --{k} {v}") + + cmd = "python scripts/train_image_baseline.py" if engine == "fingerprint" else f"python training/phase{{'fingerprint':1,'coherence':2,'sstgnn':3}.get(engine,1)}_{engine}/train.py" + flag_str = " ".join(f"--{k} {v}" for k, v in train_args.items() if not isinstance(v, bool)) + bool_flags = " ".join(f"--{k}" for k, v in train_args.items() if v is True) + print(f"\nKaggle cell command:") + print(f" !{cmd} {flag_str} {bool_flags}") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--config", default="configs/fingerprint_baseline.yaml") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/train_image_baseline.py b/scripts/train_image_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..7becbf2a5e31b11ff4e08ad6c1a02fc7b3ebd8da --- /dev/null +++ b/scripts/train_image_baseline.py @@ -0,0 +1,130 @@ +""" +scripts/train_image_baseline.py + +PRIMARY Kaggle training entrypoint for the Fingerprint engine. +This is the script called in KAGGLE.md Phase 1 training cells. + +It is a thin wrapper around training/phase1_fingerprint/train.py that: + - Accepts the same CLI args + - Sets is_kaggle=True when /kaggle/working is detected + - Handles Kaggle-specific path resolution + - Prints a clear startup summary + +Kaggle usage: + !python scripts/train_image_baseline.py \ + --data_dir /kaggle/working/processed/fingerprint \ + --output_dir /kaggle/working/checkpoints/fingerprint \ + --epochs 5 --batch_size 64 --lr 2e-5 --amp --seed 42 +""" + +from __future__ import annotations + +import argparse +import logging +import os +import sys +from pathlib import Path + +# Repo root on sys.path +sys.path.insert(0, str(Path(__file__).parent.parent)) + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%H:%M:%S", +) +log = logging.getLogger(__name__) + + +def detect_kaggle() -> bool: + return Path("/kaggle/working").exists() + + +def print_startup_summary(args: argparse.Namespace, is_kaggle: bool) -> None: + import torch + print("=" * 60) + print(" GenAI-DeepDetect — Fingerprint Engine Training") + print("=" * 60) + print(f" Environment : {'Kaggle' if is_kaggle else 'Local'}") + print(f" PyTorch : {torch.__version__}") + print(f" CUDA : {torch.cuda.is_available()}") + if torch.cuda.is_available(): + print(f" GPU : {torch.cuda.get_device_name(0)}") + print(f" VRAM : {torch.cuda.get_device_properties(0).total_memory // (1024**3)}GB") + print(f" Data dir : {args.data_dir}") + print(f" Output dir : {args.output_dir}") + print(f" Epochs : {args.epochs}") + print(f" Batch size : {args.batch_size}") + print(f" LR : {args.lr}") + print(f" AMP : {args.amp}") + print(f" Seed : {args.seed}") + print("=" * 60) + + # Estimate time + epochs = args.epochs + batch_size = args.batch_size + est_minutes = int(epochs * (180000 / batch_size) / 60 / 10) # rough 140k dataset + print(f" Est. time : ~{est_minutes} min on P100 for {epochs} epochs") + print("=" * 60) + + +def verify_data_dir(data_dir: str) -> bool: + """Check the dataset directory looks like it was built correctly.""" + root = Path(data_dir) + ok = True + for split in ["train", "val"]: + for label in ["real", "fake"]: + d = root / split / label + if not d.exists(): + log.warning(f"Missing: {d}") + ok = False + else: + count = len(list(d.glob("*.jpg")) + list(d.glob("*.png"))) + log.info(f" {split}/{label}: {count} images") + return ok + + +def main() -> None: + p = argparse.ArgumentParser(description="Train Fingerprint engine (Kaggle entrypoint)") + p.add_argument("--data_dir", required=True) + p.add_argument("--output_dir", required=True) + p.add_argument("--epochs", type=int, default=30) + p.add_argument("--batch_size", type=int, default=64) + p.add_argument("--lr", type=float, default=2e-5) + p.add_argument("--weight_decay", type=float, default=0.01) + p.add_argument("--warmup_steps", type=int, default=500) + p.add_argument("--grad_accum", type=int, default=1) + p.add_argument("--workers", type=int, default=4) + p.add_argument("--patience", type=int, default=5) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--amp", action="store_true", default=False) + p.add_argument("--wandb", action="store_true", default=False) + p.add_argument("--resume", default=None) + p.add_argument("--skip_verify", action="store_true", default=False, + help="Skip dataset verification (faster startup on Kaggle)") + args = p.parse_args() + + is_kaggle = detect_kaggle() + if is_kaggle: + # On Kaggle: more workers may cause issues + args.workers = min(args.workers, 2) + + print_startup_summary(args, is_kaggle) + + # Verify dataset exists + if not args.skip_verify: + log.info("Verifying dataset ...") + if not verify_data_dir(args.data_dir): + log.error( + f"Dataset incomplete at {args.data_dir}. " + "Run build_dataset.py first (see KAGGLE.md Phase 1 Cell 5)." + ) + sys.exit(1) + + # Delegate to the full training module + from training.phase1_fingerprint.train import main as train_main + train_main(args) + + +if __name__ == "__main__": + main() diff --git a/spaces/README.md b/spaces/README.md new file mode 100644 index 0000000000000000000000000000000000000000..827da0a94eb33d73324a59af18a9736fd2d161f4 --- /dev/null +++ b/spaces/README.md @@ -0,0 +1,19 @@ +--- +title: GenAI DeepDetect +emoji: "🔍" +colorFrom: gray +colorTo: indigo +sdk: docker +app_port: 7860 +pinned: false +--- + +# GenAI-DeepDetect + +Docker-based Hugging Face Space for multimodal deepfake detection. + +This Space runs the FastAPI service from `src/api/main.py` and exposes: + +- `GET /health` +- `POST /detect/image` +- `POST /detect/video` diff --git a/spaces/app.py b/spaces/app.py new file mode 100644 index 0000000000000000000000000000000000000000..9e351922bcbc92b0559099a203490ff9f3d4df98 --- /dev/null +++ b/spaces/app.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +import os + +os.environ.setdefault("MODEL_CACHE_DIR", "/data/models") +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") + +import uvicorn + +from src.api.main import app + + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=7860, workers=1) diff --git a/spaces/examples/.gitkeep b/spaces/examples/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/spaces/requirements.txt b/spaces/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e886535a7f96eff21a364d66edb794cda5f0606e --- /dev/null +++ b/spaces/requirements.txt @@ -0,0 +1,7 @@ +gradio>=4.36.1 +torch>=2.1.0 +torchvision>=0.16.0 +transformers>=4.40.0 +Pillow>=10.0.0 +numpy>=1.26.0 +httpx>=0.27.0 diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/api/__init__.py b/src/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/api/demo_page.py b/src/api/demo_page.py new file mode 100644 index 0000000000000000000000000000000000000000..068b7def0984519135d406ea320f6a7f8e3e9e4b --- /dev/null +++ b/src/api/demo_page.py @@ -0,0 +1,48 @@ +"""src/api/demo_page.py — Minimal HTML demo page served at GET /""" + + +def render_demo() -> str: + return """ + + + + +GenAI-DeepDetect API + + + +
+
GenAI-DeepDetect · v1.0.0
+

Multimodal Deepfake Detection API

+

Three independent engines — Fingerprint (ViT), Coherence (lip-audio sync), SSTGNN (landmark graph) — fused into a single verdict.

+
    +
  • GET/healthStatus check
    • +
  • POST/detect/imageAnalyse image (max 20MB)
    • +
  • POST/detect/videoAnalyse video (max 500MB)
    • +
+
+ API docs + ReDoc + Health +
+
+ +""" diff --git a/src/api/main.py b/src/api/main.py new file mode 100644 index 0000000000000000000000000000000000000000..d976cd026a6e3544c2a9b89c7f0cba93f79fff86 --- /dev/null +++ b/src/api/main.py @@ -0,0 +1,438 @@ +from __future__ import annotations + +import asyncio +import io +import logging +import os +import sys +import tempfile +import time +from pathlib import Path + +import numpy as np +from dotenv import load_dotenv +from fastapi import FastAPI, File, HTTPException, UploadFile +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +from PIL import Image + +from src.engines.coherence.engine import CoherenceEngine +from src.engines.fingerprint.engine import FingerprintEngine +from src.engines.sstgnn.engine import SSTGNNEngine +from src.explainability.explainer import explain +from src.fusion.fuser import fuse +from src.services.hf_inference_client import HFInferenceClient, HFInferenceUnavailable +from src.services.inference_router import ( + get_inference_backend, + is_runpod_configured, + route_inference, +) +from src.types import DetectionResponse, EngineResult + +logger = logging.getLogger(__name__) + +# Load local development environment values from .env when present. +load_dotenv() + + +def _is_test_mode() -> bool: + return ( + os.environ.get("GENAI_SKIP_MODEL_LOAD", "").strip().lower() + in {"1", "true", "yes", "on"} + or "PYTEST_CURRENT_TEST" in os.environ + or "pytest" in sys.modules + ) + + +if _is_test_mode(): + os.environ.setdefault("GENAI_SKIP_MODEL_LOAD", "1") + + +app = FastAPI(title="GenAI-DeepDetect", version="1.0.0") +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +_fp = FingerprintEngine() +_co = CoherenceEngine() +_st = SSTGNNEngine() +_hf = HFInferenceClient() + +MAX_IMAGE_MB = int(os.environ.get("MAX_IMAGE_SIZE_MB", 20)) +MAX_VIDEO_MB = int(os.environ.get("MAX_VIDEO_SIZE_MB", 100)) +MAX_FRAMES = int(os.environ.get("MAX_VIDEO_FRAMES", 300)) + +IMAGE_TYPES = {"image/jpeg", "image/png", "image/webp", "image/bmp", "image/gif"} +VIDEO_TYPES = {"video/mp4", "video/quicktime", "video/x-msvideo", "video/webm", "video/avi"} + +SUPPORTED_GENERATORS = [ + "real", + "unknown_gan", + "stable_diffusion", + "midjourney", + "dall_e", + "flux", + "firefly", + "imagen", +] + + +def _model_inventory() -> dict[str, object]: + return { + "fingerprint": { + "primary_detector": "Organika/sdxl-detector", + "backup_detector": "haywoodsloan/ai-image-detector-deploy", + "attribution_model": "openai/clip-vit-large-patch14", + }, + "coherence": { + "hf_fallback_model": os.environ.get("COHERENCE_HF_MODEL_ID", "Wvolf/ViT_Deepfake_Detection"), + "facial_landmarks": "mediapipe FaceMesh/FaceLandmarker", + "temporal_embedding": "facenet-pytorch InceptionResnetV1(vggface2) when available", + }, + "sstgnn": { + "primary_detector": "dima806/deepfake_vs_real_image_detection", + "backup_detector": "prithivMLmods/Deep-Fake-Detector-Model", + "graph_component": "scipy.spatial.Delaunay + MediaPipe landmarks", + }, + "generator_labels": SUPPORTED_GENERATORS, + } + + +@app.get("/", response_class=HTMLResponse) +async def root() -> HTMLResponse: + return HTMLResponse("

GenAI-DeepDetect API

See /docs

") + + +@app.on_event("startup") +async def preload() -> None: + if _is_test_mode(): + logger.info("Skipping startup preload in test mode") + return + + logger.info("Preloading models...") + # Keep model imports/loads sequential to avoid lazy-import race issues. + await asyncio.to_thread(_fp._ensure) + await asyncio.to_thread(_co._ensure) + await asyncio.to_thread(_st._ensure) + logger.info("Model preload complete") + + +@app.get("/health") +async def health() -> dict: + return { + "status": "ok", + "version": "1.0.0", + "engines": ["fingerprint", "coherence", "sstgnn"], + "inference_backend": get_inference_backend(), + "runpod_configured": is_runpod_configured(), + } + + +@app.get("/health/models") +async def health_models() -> dict[str, object]: + """Return the pretrained model inventory used by each engine.""" + return _model_inventory() + + +def _extract_frames(path: str) -> list[np.ndarray]: + try: + import cv2 + except Exception as exc: + raise RuntimeError(f"OpenCV unavailable: {exc}") from exc + + cap = cv2.VideoCapture(path) + total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + step = max(1, total // MAX_FRAMES) if total > 0 else 1 + + frames: list[np.ndarray] = [] + index = 0 + while True: + ret, frame = cap.read() + if not ret: + break + if index % step == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + index += 1 + if len(frames) >= MAX_FRAMES: + break + + cap.release() + return frames + + +def _assign_processing_time(results: list[EngineResult], ms: float) -> list[EngineResult]: + for result in results: + result.processing_time_ms = round(ms, 2) + return results + + +def _fallback_explanation(verdict: str, confidence: float, generator: str) -> str: + return ( + f"Content classified as {verdict} with {confidence:.0%} confidence. " + f"Attributed generator: {generator}." + ) + + +def _hf_fake_score(preds: list[dict]) -> float: + if not preds: + return 0.5 + + fake_keywords = ("fake", "deepfake", "generated", "synthetic", "artificial", "ai") + real_keywords = ("real", "authentic", "human") + + fake_best = 0.0 + real_best = 0.0 + for pred in preds: + label = str(pred.get("label", "")).strip().lower() + score = float(pred.get("score", 0.0)) + if any(keyword in label for keyword in fake_keywords): + fake_best = max(fake_best, score) + if any(keyword in label for keyword in real_keywords): + real_best = max(real_best, score) + + if fake_best == 0.0 and real_best == 0.0: + return 0.5 + if fake_best == 0.0: + return float(np.clip(1.0 - real_best, 0.0, 1.0)) + return float(np.clip(fake_best, 0.0, 1.0)) + + +def _hf_generator_label(preds: list[dict], verdict: str) -> str: + if verdict != "FAKE": + return "real" + labels = " ".join(str(pred.get("label", "")).lower() for pred in preds) + for candidate in SUPPORTED_GENERATORS: + if candidate == "real": + continue + if candidate.replace("_", " ") in labels or candidate in labels: + return candidate + return "unknown_gan" + + +def _build_hf_response(preds: list[dict], elapsed_ms: float, media_type: str) -> DetectionResponse: + fake_score = _hf_fake_score(preds) + verdict = "FAKE" if fake_score > 0.5 else "REAL" + confidence = fake_score if verdict == "FAKE" else (1.0 - fake_score) + generator = _hf_generator_label(preds, verdict) + + top_label = str(preds[0].get("label", "unknown")) if preds else "unknown" + explanation = ( + f"Hugging Face serverless ({media_type}) top label: {top_label}. " + f"Classified as {verdict} with {confidence:.0%} confidence." + ) + + engine_result = EngineResult( + engine="hf_serverless", + verdict=verdict, + confidence=confidence, + attributed_generator=generator, + explanation=explanation, + processing_time_ms=elapsed_ms, + ) + + return DetectionResponse( + verdict=verdict, + confidence=confidence, + attributed_generator=generator, + explanation=explanation, + processing_time_ms=elapsed_ms, + engine_breakdown=[engine_result], + ) + + +async def _hf_detect_image(data: bytes) -> DetectionResponse: + t0 = time.monotonic() + preds = await _hf.classify_image(data, timeout=45.0) + elapsed_ms = (time.monotonic() - t0) * 1000 + return _build_hf_response(preds, elapsed_ms, media_type="image") + + +async def _hf_detect_video(data: bytes) -> DetectionResponse: + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp: + tmp.write(data) + tmp_path = tmp.name + + try: + frames = await asyncio.to_thread(_extract_frames, tmp_path) + finally: + Path(tmp_path).unlink(missing_ok=True) + + if not frames: + raise HTTPException(status_code=422, detail="Could not extract frames") + + keyframe = Image.fromarray(frames[0]) + buf = io.BytesIO() + keyframe.save(buf, format="JPEG") + return await _hf_detect_image(buf.getvalue()) + + +async def _ensure_models_loaded() -> None: + if _is_test_mode(): + return + await asyncio.to_thread(_fp._ensure) + await asyncio.to_thread(_co._ensure) + await asyncio.to_thread(_st._ensure) + + +@app.post("/detect/image", response_model=DetectionResponse) +async def detect_image(file: UploadFile = File(...)) -> DetectionResponse: + t0 = time.monotonic() + + content_type = (file.content_type or "").split(";")[0].strip().lower() + if content_type not in IMAGE_TYPES: + raise HTTPException(status_code=415, detail=f"Unsupported type: {file.content_type}") + + data = await file.read() + if len(data) > MAX_IMAGE_MB * 1024 * 1024: + raise HTTPException(status_code=413, detail="File too large") + + backend = get_inference_backend() + if backend == "hf" and not _is_test_mode(): + try: + return await _hf_detect_image(data) + except HFInferenceUnavailable as exc: + logger.warning("HF image route failed, trying RunPod fallback: %s", exc) + except Exception as exc: + logger.warning("HF image route unexpected error, trying RunPod fallback: %s", exc) + + if is_runpod_configured(): + try: + return await route_inference(data, "image") + except Exception as exc: + raise HTTPException( + status_code=503, + detail=f"Hugging Face and RunPod failed for image inference: {exc}", + ) from exc + + raise HTTPException( + status_code=503, + detail="Hugging Face inference failed and RunPod is not configured.", + ) + + if ( + backend == "runpod" + and not _is_test_mode() + and is_runpod_configured() + ): + try: + return await route_inference(data, "image") + except Exception as exc: + logger.warning("RunPod image route failed, falling back to local image inference: %s", exc) + + try: + image = Image.open(io.BytesIO(data)).convert("RGB") + except Exception as exc: + raise HTTPException(status_code=422, detail=f"Could not decode image: {exc}") from exc + + await _ensure_models_loaded() + + fp, co, st = await asyncio.gather( + asyncio.to_thread(_fp.run, image), + asyncio.to_thread(_co.run, image), + asyncio.to_thread(_st.run, image), + ) + + elapsed_ms = (time.monotonic() - t0) * 1000 + engine_results = _assign_processing_time([fp, co, st], elapsed_ms) + + verdict, conf, generator = fuse(engine_results, is_video=False) + if _is_test_mode(): + explanation = _fallback_explanation(verdict, conf, generator) + else: + explanation = await asyncio.to_thread(explain, verdict, conf, engine_results, generator) + + return DetectionResponse( + verdict=verdict, + confidence=conf, + attributed_generator=generator, + explanation=explanation, + processing_time_ms=elapsed_ms, + engine_breakdown=engine_results, + ) + + +@app.post("/detect/video", response_model=DetectionResponse) +async def detect_video(file: UploadFile = File(...)) -> DetectionResponse: + t0 = time.monotonic() + + content_type = (file.content_type or "").split(";")[0].strip().lower() + if content_type not in VIDEO_TYPES: + raise HTTPException(status_code=415, detail=f"Unsupported type: {file.content_type}") + + data = await file.read() + if len(data) > MAX_VIDEO_MB * 1024 * 1024: + raise HTTPException(status_code=413, detail="File too large") + + backend = get_inference_backend() + if backend == "hf" and not _is_test_mode(): + try: + return await _hf_detect_video(data) + except HFInferenceUnavailable as exc: + logger.warning("HF video route failed, trying RunPod fallback: %s", exc) + except Exception as exc: + logger.warning("HF video route unexpected error, trying RunPod fallback: %s", exc) + + if is_runpod_configured(): + try: + return await route_inference(data, "video") + except Exception as exc: + raise HTTPException( + status_code=503, + detail=f"Hugging Face and RunPod failed for video inference: {exc}", + ) from exc + + raise HTTPException( + status_code=503, + detail="Hugging Face inference failed and RunPod is not configured.", + ) + + should_try_runpod = ( + backend == "runpod" + or (backend == "auto" and len(data) > 20 * 1024 * 1024) + ) + if should_try_runpod and not _is_test_mode() and is_runpod_configured(): + try: + return await route_inference(data, "video") + except Exception as exc: + logger.warning("RunPod route failed, falling back to local video inference: %s", exc) + + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp: + tmp.write(data) + tmp_path = tmp.name + + try: + frames = await asyncio.to_thread(_extract_frames, tmp_path) + finally: + Path(tmp_path).unlink(missing_ok=True) + + if not frames: + raise HTTPException(status_code=422, detail="Could not extract frames") + + await _ensure_models_loaded() + + fp, co, st = await asyncio.gather( + asyncio.to_thread(_fp.run_video, frames), + asyncio.to_thread(_co.run_video, frames), + asyncio.to_thread(_st.run_video, frames), + ) + + elapsed_ms = (time.monotonic() - t0) * 1000 + engine_results = _assign_processing_time([fp, co, st], elapsed_ms) + + verdict, conf, generator = fuse(engine_results, is_video=True) + if _is_test_mode(): + explanation = _fallback_explanation(verdict, conf, generator) + else: + explanation = await asyncio.to_thread(explain, verdict, conf, engine_results, generator) + + return DetectionResponse( + verdict=verdict, + confidence=conf, + attributed_generator=generator, + explanation=explanation, + processing_time_ms=elapsed_ms, + engine_breakdown=engine_results, + ) diff --git a/src/continual/__init__.py b/src/continual/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ff64c8883e514a39f1cdf5b8b6627695b121d449 --- /dev/null +++ b/src/continual/__init__.py @@ -0,0 +1 @@ +# src/continual — EWC (Elastic Weight Consolidation) continual learning diff --git a/src/continual/ewc.py b/src/continual/ewc.py new file mode 100644 index 0000000000000000000000000000000000000000..d4561bd208c2df486bc5515552d48bfc05d11328 --- /dev/null +++ b/src/continual/ewc.py @@ -0,0 +1,147 @@ +""" +src/continual/ewc.py — Elastic Weight Consolidation (EWC) + +Implements EWC penalty term to prevent catastrophic forgetting when +fine-tuning on new generator classes. + +EWC loss: + L_total = L_task_new + λ * Σ_i F_i * (θ_i - θ*_i)² + +where: + F_i = diagonal Fisher information for parameter i on the *old* task + θ*_i = parameter i's value immediately after old-task training + λ = importance weight (default 400, tunable in configs/) + +Reference: + Kirkpatrick et al. "Overcoming catastrophic forgetting in neural networks." + PNAS 2017. https://arxiv.org/abs/1612.00796 + +Usage: + ewc = EWC(model, dataloader_old_task) + loss = ewc.penalty(model) # add to task-specific loss before backward() +""" +from __future__ import annotations + +import copy +from typing import Iterator, Optional + +import torch +import torch.nn as nn +from torch import Tensor +from torch.utils.data import DataLoader + + +class EWC: + """ + Elastic Weight Consolidation penalty tracker. + + Parameters + ---------- + model: + PyTorch module whose important weights we want to preserve. + dataloader: + DataLoader over the *previous* task's training data (used to + estimate the diagonal Fisher information). + criterion: + Loss function used to compute per-sample gradients. + Defaults to CrossEntropyLoss. + num_samples: + Maximum number of samples used to estimate Fisher. Fewer samples + = faster, noisier estimate. + importance: + λ — EWC penalty weight. Higher → less forgetting, slower adaptation. + """ + + def __init__( + self, + model: nn.Module, + dataloader: DataLoader, + criterion: Optional[nn.Module] = None, + num_samples: int = 200, + importance: float = 400.0, + ) -> None: + self.model = model + self.importance = importance + self._criterion = criterion or nn.CrossEntropyLoss() + + # Snapshot θ* (old-task parameters) + self._params_star: dict[str, Tensor] = { + name: param.clone().detach() + for name, param in model.named_parameters() + if param.requires_grad + } + + # Estimate diagonal Fisher + self._fisher: dict[str, Tensor] = self._estimate_fisher( + model, dataloader, num_samples + ) + + # ------------------------------------------------------------------ + # Public + # ------------------------------------------------------------------ + + def penalty(self, model: nn.Module) -> Tensor: + """ + Compute the EWC penalty for the *current* model parameters. + + Returns a scalar tensor that can be added to the task loss. + """ + device = next(model.parameters()).device + loss = torch.tensor(0.0, device=device) + + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + if name not in self._fisher: + continue + fisher = self._fisher[name].to(device) + star = self._params_star[name].to(device) + loss += (fisher * (param - star).pow(2)).sum() + + return self.importance * loss + + # ------------------------------------------------------------------ + # Private + # ------------------------------------------------------------------ + + def _estimate_fisher( + self, + model: nn.Module, + dataloader: DataLoader, + num_samples: int, + ) -> dict[str, Tensor]: + """Estimate diagonal Fisher via squared gradients on old-task data.""" + fisher: dict[str, Tensor] = { + name: torch.zeros_like(param) + for name, param in model.named_parameters() + if param.requires_grad + } + + model.eval() + n_seen = 0 + + for batch in dataloader: + if n_seen >= num_samples: + break + + images, labels = batch + device = next(model.parameters()).device + images, labels = images.to(device), labels.to(device) + + model.zero_grad() + output = model(images) + loss = self._criterion(output, labels) + loss.backward() + + for name, param in model.named_parameters(): + if param.requires_grad and param.grad is not None: + fisher[name] += param.grad.detach().pow(2) + + n_seen += images.size(0) + + # Normalize + if n_seen > 0: + for name in fisher: + fisher[name] /= n_seen + + return fisher diff --git a/src/continual/registry.py b/src/continual/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..10a6085e6987371b1629201ed7f4ed6d9ccb2f03 --- /dev/null +++ b/src/continual/registry.py @@ -0,0 +1,121 @@ +""" +src/continual/registry.py — Continual-learning task registry + +Persists a JSON list of registered generator classes (tasks) to disk so +training scripts and the API can stay in sync. + +Schema (per entry): + { + "label": "sora", # generator label string + "sample_path": "data/sora/...", # representative sample path + "registered_at": "2025-01-01T00:00:00", + "weight_path": null # filled after EWC checkpoint is saved + } + +Usage: + registry = TaskRegistry() + registry.register("sora", "data/sora/frame_001.jpg") + tasks = registry.all_tasks() +""" +from __future__ import annotations + +import json +import logging +import os +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + +_DEFAULT_REGISTRY_PATH = Path("data/continual_registry.json") + + +class TaskRegistry: + """ + Simple JSON-backed task registry for continual-learning experiments. + + Parameters + ---------- + path: + Path to the JSON registry file. Created on first write. + """ + + def __init__(self, path: Path | str = _DEFAULT_REGISTRY_PATH) -> None: + self._path = Path(path) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def register( + self, + label: str, + sample_path: str, + weight_path: str | None = None, + ) -> dict[str, Any]: + """ + Register a new generator-class task. + + Returns the new registry entry dict. + Raises ValueError if *label* already registered. + """ + tasks = self._load() + existing = [t for t in tasks if t["label"] == label] + if existing: + raise ValueError( + f"Task '{label}' already registered. " + f"Use update() to modify it." + ) + + entry: dict[str, Any] = { + "label": label, + "sample_path": sample_path, + "registered_at": datetime.now(timezone.utc).isoformat(), + "weight_path": weight_path, + } + tasks.append(entry) + self._save(tasks) + logger.info("Registered continual task: %s", label) + return entry + + def update(self, label: str, **fields: Any) -> dict[str, Any]: + """Update fields on an existing task entry.""" + tasks = self._load() + for task in tasks: + if task["label"] == label: + task.update(fields) + self._save(tasks) + return task + raise KeyError(f"Task '{label}' not found in registry.") + + def all_tasks(self) -> list[dict[str, Any]]: + """Return all registered tasks.""" + return self._load() + + def get(self, label: str) -> dict[str, Any] | None: + """Return a single task by label, or None.""" + for task in self._load(): + if task["label"] == label: + return task + return None + + # ------------------------------------------------------------------ + # Internal + # ------------------------------------------------------------------ + + def _load(self) -> list[dict[str, Any]]: + if not self._path.exists(): + return [] + try: + return json.loads(self._path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + logger.warning("Registry file corrupt; starting fresh.") + return [] + + def _save(self, tasks: list[dict[str, Any]]) -> None: + self._path.parent.mkdir(parents=True, exist_ok=True) + self._path.write_text( + json.dumps(tasks, indent=2, default=str), + encoding="utf-8", + ) diff --git a/src/engines/__init__.py b/src/engines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fd5f9e6daf7a241328212c571ae9dba1d98bd769 --- /dev/null +++ b/src/engines/__init__.py @@ -0,0 +1,12 @@ +from .fingerprint import FingerprintEngine, FingerprintDetector +from .coherence import CoherenceEngine, CoherenceDetector +from .sstgnn import SSTGNNEngine, SSTGNNDetector + +__all__ = [ + "FingerprintEngine", + "FingerprintDetector", + "CoherenceEngine", + "CoherenceDetector", + "SSTGNNEngine", + "SSTGNNDetector", +] diff --git a/src/engines/coherence/__init__.py b/src/engines/coherence/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6ccbe124e6137240e684a187f1d27d69d082c4bf --- /dev/null +++ b/src/engines/coherence/__init__.py @@ -0,0 +1,4 @@ +from .engine import CoherenceEngine +from .detector import CoherenceDetector + +__all__ = ["CoherenceEngine", "CoherenceDetector"] diff --git a/src/engines/coherence/detector.py b/src/engines/coherence/detector.py new file mode 100644 index 0000000000000000000000000000000000000000..87843c58827b57496629ecbad8adcfb421759190 --- /dev/null +++ b/src/engines/coherence/detector.py @@ -0,0 +1,75 @@ +"""Compatibility wrapper around `CoherenceEngine`. + +New code should import `src.engines.coherence.engine.CoherenceEngine` directly. +""" +from __future__ import annotations + +import os +import tempfile + +from src.types import EngineResult + +from .engine import CoherenceEngine + + +class CoherenceDetector(CoherenceEngine): + threshold = 0.5 + + def detect_bytes(self, video_bytes: bytes) -> EngineResult: + frames = self._extract_video_frames(video_bytes) + if not frames: + return self._error_result(0.0) + try: + return self.run_video(frames) + except Exception: + return self._error_result(0.0) + + def _extract_video_frames(self, video_bytes: bytes) -> list: + try: + import cv2 + except Exception: + return [] + + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp: + tmp.write(video_bytes) + tmp_path = tmp.name + + frames = [] + try: + cap = cv2.VideoCapture(tmp_path) + index = 0 + while True: + ok, frame = cap.read() + if not ok: + break + if index % 2 == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + index += 1 + if len(frames) >= 64: + break + cap.release() + return frames + finally: + os.unlink(tmp_path) + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="coherence", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="N/A - coherence analysis requires video input.", + processing_time_ms=0.0, + ) + + @staticmethod + def _error_result(elapsed_s: float) -> EngineResult: + return EngineResult( + engine="coherence", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="Coherence engine encountered an error - result is inconclusive.", + processing_time_ms=elapsed_s * 1000.0, + ) diff --git a/src/engines/coherence/engine.py b/src/engines/coherence/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..c3473e79c82ea42128a1d66233865666af505893 --- /dev/null +++ b/src/engines/coherence/engine.py @@ -0,0 +1,387 @@ +from __future__ import annotations + +import logging +import os +import threading +import time +import urllib.request +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image + +from src.types import EngineResult + +logger = logging.getLogger(__name__) + +_lock = threading.Lock() +_load_attempted = False +_mtcnn = None +_resnet = None +_face_mesh = None +_torch = None +_hf_detector = None + + +def _skip_model_loads() -> bool: + return os.environ.get("GENAI_SKIP_MODEL_LOAD", "").strip().lower() in { + "1", + "true", + "yes", + "on", + } + + +def _get_pipeline(): + try: + from transformers import pipeline as hf_pipeline # type: ignore + except Exception: + from transformers.pipelines import pipeline as hf_pipeline # type: ignore + return hf_pipeline + + +class _TasksFaceMeshAdapter: + def __init__(self, mp_module, landmarker) -> None: + self._mp = mp_module + self._landmarker = landmarker + + def process(self, frame: np.ndarray): + mp_image = self._mp.Image(image_format=self._mp.ImageFormat.SRGB, data=frame) + result = self._landmarker.detect(mp_image) + + class _Face: + def __init__(self, landmarks): + self.landmark = landmarks + + class _Result: + def __init__(self, faces): + self.multi_face_landmarks = faces + + faces = [_Face(landmarks) for landmarks in getattr(result, "face_landmarks", [])] + return _Result(faces) + + +def _ensure_face_landmarker_asset() -> Path: + cache_dir = Path(os.environ.get("MODEL_CACHE_DIR", "/tmp/models")) + model_path = cache_dir / "mediapipe" / "face_landmarker.task" + if model_path.exists(): + return model_path + + model_path.parent.mkdir(parents=True, exist_ok=True) + url = os.environ.get( + "MEDIAPIPE_FACE_LANDMARKER_URL", + ( + "https://storage.googleapis.com/mediapipe-models/face_landmarker/" + "face_landmarker/float16/latest/face_landmarker.task" + ), + ) + urllib.request.urlretrieve(url, model_path) + return model_path + + +def _build_face_mesh(): + import mediapipe as mp # type: ignore + + if hasattr(mp, "solutions"): + return mp.solutions.face_mesh.FaceMesh( + static_image_mode=False, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5, + ) + + from mediapipe.tasks import python as mp_tasks_python # type: ignore + from mediapipe.tasks.python import vision # type: ignore + + model_path = _ensure_face_landmarker_asset() + options = vision.FaceLandmarkerOptions( + base_options=mp_tasks_python.BaseOptions(model_asset_path=str(model_path)), + running_mode=vision.RunningMode.IMAGE, + num_faces=1, + ) + landmarker = vision.FaceLandmarker.create_from_options(options) + return _TasksFaceMeshAdapter(mp, landmarker) + + +def _build_image_classifier(model_id: str) -> Any: + pipeline = _get_pipeline() + + cache_dir = os.environ.get("MODEL_CACHE_DIR", "/tmp/models") + try: + return pipeline( + "image-classification", + model=model_id, + model_kwargs={"cache_dir": cache_dir}, + ) + except Exception: + return pipeline("image-classification", model=model_id) + + +def _load() -> None: + global _mtcnn, _resnet, _face_mesh, _load_attempted, _torch, _hf_detector + if _load_attempted: + return + + _load_attempted = True + if _skip_model_loads(): + logger.info("Skipping coherence model load (GENAI_SKIP_MODEL_LOAD=1)") + return + + logger.info("Loading coherence models...") + + try: + _face_mesh = _build_face_mesh() + except Exception as exc: + logger.warning("Coherence FaceMesh unavailable: %s", exc) + + try: + from facenet_pytorch import InceptionResnetV1, MTCNN # type: ignore + + _mtcnn = MTCNN(keep_all=False, device="cpu") + _resnet = InceptionResnetV1(pretrained="vggface2").eval() + + try: + import torch # type: ignore + + _torch = torch + except Exception: + _torch = None + + except Exception as exc: + logger.warning("Coherence embedding model load failed, using heuristic-only mode: %s", exc) + + try: + model_id = os.environ.get("COHERENCE_HF_MODEL_ID", "Wvolf/ViT_Deepfake_Detection") + _hf_detector = _build_image_classifier(model_id) + except Exception as exc: + logger.warning("Coherence HF fallback model unavailable: %s", exc) + + logger.info("Coherence model load attempt complete") + + +class CoherenceEngine: + def _ensure(self) -> None: + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + t0 = time.perf_counter() + self._ensure() + + frame = np.array(image.convert("RGB")) + score = self._image_score(frame) + score = float(np.clip(score * 0.6 + self._hf_image_score(image) * 0.4, 0.0, 1.0)) + + return EngineResult( + engine="coherence", + verdict="FAKE" if score > 0.5 else "REAL", + confidence=float(np.clip(score, 0.0, 1.0)), + attributed_generator=None, + explanation=f"Geometric coherence anomaly {score:.2f} (image mode).", + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + def _image_score(self, frame: np.ndarray) -> float: + if _face_mesh is None: + return 0.35 + + try: + res = _face_mesh.process(frame) + if not res.multi_face_landmarks: + return 0.35 + + lms = res.multi_face_landmarks[0].landmark + h, w = frame.shape[:2] + + def pt(index: int) -> np.ndarray: + return np.array([lms[index].x * w, lms[index].y * h], dtype=np.float32) + + lew = np.linalg.norm(pt(33) - pt(133)) + rew = np.linalg.norm(pt(362) - pt(263)) + eye_ratio = float(min(lew, rew) / (max(lew, rew) + 1e-9)) + eye_score = max(0.0, (0.85 - eye_ratio) / 0.3) + + nose = pt(1) + left_dist = np.linalg.norm(nose - pt(234)) + right_dist = np.linalg.norm(nose - pt(454)) + lr = min(left_dist, right_dist) + rr = max(left_dist, right_dist) + ear_score = max(0.0, (0.90 - lr / (rr + 1e-9)) / 0.2) + + return float(np.clip(eye_score * 0.5 + ear_score * 0.5, 0.0, 1.0)) + except Exception as exc: + logger.warning("Coherence image scoring failed: %s", exc) + return 0.35 + + def run_video(self, frames: list[np.ndarray]) -> EngineResult: + t0 = time.perf_counter() + self._ensure() + + if not frames: + return EngineResult( + engine="coherence", + verdict="UNKNOWN", + confidence=0.5, + attributed_generator=None, + explanation="No frames.", + processing_time_ms=0.0, + ) + + if len(frames) < 4: + result = self.run(Image.fromarray(frames[0])) + result.explanation = "Too few frames for temporal analysis." + return result + + delta = self._embedding_variance(frames) + jerk = self._landmark_jerk(frames) + blink = self._blink_anomaly(frames) + + hf_video = self._hf_video_score(frames) + score = float(np.clip(delta * 0.35 + jerk * 0.30 + blink * 0.15 + hf_video * 0.20, 0.0, 1.0)) + + return EngineResult( + engine="coherence", + verdict="FAKE" if score > 0.5 else "REAL", + confidence=score, + attributed_generator=None, + explanation=( + f"Embedding variance {delta:.2f}, " + f"landmark jerk {jerk:.2f}, " + f"blink anomaly {blink:.2f}, " + f"hf score {hf_video:.2f}." + ), + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + def _hf_image_score(self, image: Image.Image) -> float: + if _hf_detector is None: + return 0.5 + try: + preds = _hf_detector(image) + return self._fake_score_from_preds(preds) + except Exception: + return 0.5 + + def _hf_video_score(self, frames: list[np.ndarray]) -> float: + if _hf_detector is None or not frames: + return 0.5 + values: list[float] = [] + for frame in frames[::8]: + try: + preds = _hf_detector(Image.fromarray(frame)) + values.append(self._fake_score_from_preds(preds)) + except Exception: + continue + if not values: + return 0.5 + return float(np.clip(np.mean(values), 0.0, 1.0)) + + def _fake_score_from_preds(self, preds: list[dict]) -> float: + if not preds: + return 0.5 + keywords = ("fake", "deepfake", "generated", "synthetic", "ai", "artificial") + best = 0.0 + for pred in preds: + label = str(pred.get("label", "")).lower() + score = float(pred.get("score", 0.0)) + if any(keyword in label for keyword in keywords): + best = max(best, score) + if best == 0.0: + return 0.5 + return float(np.clip(best, 0.0, 1.0)) + + def _embedding_variance(self, frames: list[np.ndarray]) -> float: + if _mtcnn is None or _resnet is None or _torch is None: + return 0.5 + + embeddings: list[np.ndarray] = [] + for frame in frames[::4]: + try: + face = _mtcnn(Image.fromarray(frame)) + if face is not None: + with _torch.no_grad(): + emb = _resnet(face.unsqueeze(0)).detach().cpu().numpy()[0] + embeddings.append(emb) + except Exception: + continue + + if len(embeddings) < 2: + return 0.5 + + deltas = [ + float(np.linalg.norm(embeddings[index + 1] - embeddings[index])) + for index in range(len(embeddings) - 1) + ] + return float(np.clip(np.var(deltas) * 8.0, 0.0, 1.0)) + + def _landmark_jerk(self, frames: list[np.ndarray]) -> float: + if _face_mesh is None: + return 0.3 + + positions: list[list[float]] = [] + for frame in frames[::2]: + try: + res = _face_mesh.process(frame) + if res.multi_face_landmarks: + lm = res.multi_face_landmarks[0].landmark + positions.append([lm[1].x, lm[1].y]) + except Exception: + continue + + if len(positions) < 4: + return 0.3 + + pos = np.array(positions, dtype=np.float32) + jerk = np.diff(pos, n=3, axis=0) + jerk_norm = np.linalg.norm(jerk, axis=1) + return float(np.clip((float(np.mean(jerk_norm)) - 0.002) / 0.008, 0.0, 1.0)) + + def _blink_anomaly(self, frames: list[np.ndarray]) -> float: + if _face_mesh is None: + return 0.3 + + left_eye = [33, 160, 158, 133, 153, 144] + right_eye = [362, 385, 387, 263, 373, 380] + + def ear(lms, idx, h: int, w: int) -> float: + pts = [np.array([lms[i].x * w, lms[i].y * h], dtype=np.float32) for i in idx] + a = np.linalg.norm(pts[1] - pts[5]) + b = np.linalg.norm(pts[2] - pts[4]) + c = np.linalg.norm(pts[0] - pts[3]) + return float((a + b) / (2.0 * c + 1e-9)) + + ears: list[float] = [] + for frame in frames: + try: + res = _face_mesh.process(frame) + if res.multi_face_landmarks: + lm = res.multi_face_landmarks[0].landmark + h, w = frame.shape[:2] + ears.append((ear(lm, left_eye, h, w) + ear(lm, right_eye, h, w)) / 2.0) + except Exception: + continue + + if len(ears) < 10: + return 0.3 + + arr = np.array(ears, dtype=np.float32) + blinks = int(np.sum(np.diff((arr < 0.21).astype(int)) > 0)) + bpm = blinks / (len(ears) / 25.0) * 60.0 + + if 8.0 <= bpm <= 25.0: + return 0.15 + if bpm < 3.0 or bpm > 35.0: + return 0.80 + return 0.45 + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="coherence", + verdict="UNKNOWN", + confidence=0.5, + attributed_generator=None, + explanation="N/A - coherence analysis requires video input.", + processing_time_ms=0.0, + ) diff --git a/src/engines/fingerprint/__init__.py b/src/engines/fingerprint/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bd174f536d30e1ab754897dfed77b71f8f08e632 --- /dev/null +++ b/src/engines/fingerprint/__init__.py @@ -0,0 +1,4 @@ +from .engine import FingerprintEngine +from .detector import FingerprintDetector + +__all__ = ["FingerprintEngine", "FingerprintDetector"] diff --git a/src/engines/fingerprint/detector.py b/src/engines/fingerprint/detector.py new file mode 100644 index 0000000000000000000000000000000000000000..85f6b2f5feacb0837f5ac165dcfb4bef3f03d711 --- /dev/null +++ b/src/engines/fingerprint/detector.py @@ -0,0 +1,85 @@ +"""Compatibility wrapper around `FingerprintEngine`. + +New code should import `src.engines.fingerprint.engine.FingerprintEngine` directly. +""" +from __future__ import annotations + +import io +import os +import tempfile + +from PIL import Image + +from src.types import EngineResult + +from .engine import FingerprintEngine + + +class FingerprintDetector(FingerprintEngine): + """Backwards-compatible detector API.""" + + threshold = 0.5 + model = object() + + def detect_bytes(self, raw_bytes: bytes, is_video: bool = False) -> EngineResult: + try: + if is_video: + frames = self._extract_video_frames(raw_bytes) + if not frames: + return self._error_result("No frames extracted from video.") + return self.run_video(frames) + + image = Image.open(io.BytesIO(raw_bytes)).convert("RGB") + return self.run(image) + except Exception as exc: + return self._error_result(str(exc)) + + def _extract_video_frames(self, raw_bytes: bytes) -> list: + try: + import cv2 + except Exception: + return [] + + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp: + tmp.write(raw_bytes) + tmp_path = tmp.name + + frames = [] + try: + cap = cv2.VideoCapture(tmp_path) + index = 0 + while True: + ok, frame = cap.read() + if not ok: + break + if index % 8 == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + index += 1 + if len(frames) >= 32: + break + cap.release() + return frames + finally: + os.unlink(tmp_path) + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="fingerprint", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="N/A", + processing_time_ms=0.0, + ) + + @staticmethod + def _error_result(reason: str) -> EngineResult: + return EngineResult( + engine="fingerprint", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation=f"Fingerprint engine error: {reason}", + processing_time_ms=0.0, + ) diff --git a/src/engines/fingerprint/engine.py b/src/engines/fingerprint/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..925827ebd1a4668f15f4a1899d3bb74aa9fef4b5 --- /dev/null +++ b/src/engines/fingerprint/engine.py @@ -0,0 +1,230 @@ +from __future__ import annotations + +import io +import logging +import os +import threading +import time +from collections import Counter +from typing import Any + +import numpy as np +from PIL import Image + +from src.types import EngineResult + +logger = logging.getLogger(__name__) +CACHE = os.environ.get("MODEL_CACHE_DIR", "/tmp/models") + +GENERATOR_PROMPTS = { + "real": "a real photograph taken by a camera with natural lighting", + "unknown_gan": "a GAN-generated image with checkerboard artifacts and blurry edges", + "stable_diffusion": "a Stable Diffusion image with painterly soft textures", + "midjourney": "a Midjourney image with cinematic dramatic lighting and hyperdetail", + "dall_e": "a DALL-E image with clean illustration-style and smooth gradients", + "flux": "a FLUX model image with photorealistic precision and sharp detail", + "firefly": "an Adobe Firefly image with commercial stock-photo aesthetics", + "imagen": "a Google Imagen image with precise photorealistic rendering", +} + +_FAKE_KEYWORDS = ("artificial", "fake", "ai", "generated", "deepfake", "synthetic") + +_lock = threading.Lock() +_load_attempted = False +_detector = None +_clip_zeroshot = None +_backup = None + + +def _skip_model_loads() -> bool: + return os.environ.get("GENAI_SKIP_MODEL_LOAD", "").strip().lower() in { + "1", + "true", + "yes", + "on", + } + + +def _get_pipeline(): + try: + from transformers import pipeline as hf_pipeline # type: ignore + except Exception: + from transformers.pipelines import pipeline as hf_pipeline # type: ignore + return hf_pipeline + + +def _build_image_classifier(model_id: str) -> Any: + pipeline = _get_pipeline() + + # transformers>=5 changed some pipeline kwargs behavior; keep both paths. + try: + return pipeline( + "image-classification", + model=model_id, + model_kwargs={"cache_dir": CACHE}, + ) + except Exception: + return pipeline("image-classification", model=model_id) + + +def _build_zero_shot_image_classifier(model_id: str) -> Any: + pipeline = _get_pipeline() + + try: + return pipeline( + "zero-shot-image-classification", + model=model_id, + model_kwargs={"cache_dir": CACHE}, + ) + except Exception: + return pipeline("zero-shot-image-classification", model=model_id) + + +def _load() -> None: + global _detector, _clip_zeroshot, _backup, _load_attempted + if _load_attempted: + return + + _load_attempted = True + if _skip_model_loads(): + logger.info("Skipping fingerprint model load (GENAI_SKIP_MODEL_LOAD=1)") + return + + logger.info("Loading fingerprint models...") + + try: + _detector = _build_image_classifier("Organika/sdxl-detector") + _clip_zeroshot = _build_zero_shot_image_classifier("openai/clip-vit-large-patch14") + + try: + _backup = _build_image_classifier("haywoodsloan/ai-image-detector-deploy") + except Exception: + logger.warning("Backup fingerprint detector unavailable") + + except Exception as exc: + logger.warning("Fingerprint models unavailable: %s", exc) + + logger.info("Fingerprint model load attempt complete") + + +def _fake_score(preds: list[dict]) -> float: + if not preds: + return 0.5 + + best = 0.0 + for pred in preds: + label = str(pred.get("label", "")).lower() + score = float(pred.get("score", 0.0)) + if any(keyword in label for keyword in _FAKE_KEYWORDS): + best = max(best, score) + + if best == 0.0: + return 0.5 + return float(np.clip(best, 0.0, 1.0)) + + +class FingerprintEngine: + def _ensure(self) -> None: + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + t0 = time.perf_counter() + self._ensure() + + if image.mode != "RGB": + image = image.convert("RGB") + + fake_score = 0.5 + try: + if _detector is not None: + fake_score = _fake_score(_detector(image)) + except Exception as exc: + logger.warning("Primary detector error: %s", exc) + + if _backup is not None: + try: + backup_score = _fake_score(_backup(image)) + fake_score = float(np.clip(fake_score * 0.6 + backup_score * 0.4, 0.0, 1.0)) + except Exception: + pass + + generator = "real" + try: + if _clip_zeroshot is not None: + labels = list(GENERATOR_PROMPTS.values()) + zs = _clip_zeroshot(image, candidate_labels=labels) + if zs: + top_label = str(zs[0].get("label", "")) + for key, prompt in GENERATOR_PROMPTS.items(): + if prompt == top_label: + generator = key + break + except Exception as exc: + logger.warning("CLIP attribution error: %s", exc) + + if fake_score > 0.65 and generator == "real": + generator = "unknown_gan" + + elapsed_ms = (time.perf_counter() - t0) * 1000 + return EngineResult( + engine="fingerprint", + verdict="FAKE" if fake_score > 0.5 else "REAL", + confidence=float(np.clip(fake_score, 0.0, 1.0)), + attributed_generator=generator, + explanation=f"Binary score {fake_score:.2f}; attributed to {generator}.", + processing_time_ms=elapsed_ms, + ) + + def run_video(self, frames: list[np.ndarray]) -> EngineResult: + t0 = time.perf_counter() + if not frames: + return EngineResult( + engine="fingerprint", + verdict="UNKNOWN", + confidence=0.5, + attributed_generator="unknown_gan", + explanation="No frames.", + processing_time_ms=0.0, + ) + + keyframes = frames[::8] or [frames[0]] + results = [self.run(Image.fromarray(frame)) for frame in keyframes] + + avg = float(np.mean([r.confidence for r in results])) + generators = [ + r.attributed_generator + for r in results + if r.attributed_generator and r.attributed_generator != "real" + ] + top_gen = Counter(generators).most_common(1)[0][0] if generators else "real" + + verdict = "FAKE" if avg > 0.5 else "REAL" + if verdict == "REAL": + top_gen = "real" + elif top_gen == "real": + top_gen = "unknown_gan" + + return EngineResult( + engine="fingerprint", + verdict=verdict, + confidence=avg, + attributed_generator=top_gen, + explanation=f"Keyframe average {avg:.2f} over {len(keyframes)} frames.", + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="fingerprint", + verdict="UNKNOWN", + confidence=0.5, + attributed_generator=None, + explanation="N/A", + processing_time_ms=0.0, + ) + + def run_bytes(self, data: bytes) -> EngineResult: + image = Image.open(io.BytesIO(data)).convert("RGB") + return self.run(image) diff --git a/src/engines/sstgnn/__init__.py b/src/engines/sstgnn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3f6a250cd3c75c503d2789d2b809392894b9196f --- /dev/null +++ b/src/engines/sstgnn/__init__.py @@ -0,0 +1,4 @@ +from .engine import SSTGNNEngine +from .detector import SSTGNNDetector + +__all__ = ["SSTGNNEngine", "SSTGNNDetector"] diff --git a/src/engines/sstgnn/detector.py b/src/engines/sstgnn/detector.py new file mode 100644 index 0000000000000000000000000000000000000000..d07274f24d62b39105108b7453a34885daf58627 --- /dev/null +++ b/src/engines/sstgnn/detector.py @@ -0,0 +1,94 @@ +"""Compatibility wrapper around `SSTGNNEngine`. + +New code should import `src.engines.sstgnn.engine.SSTGNNEngine` directly. +""" +from __future__ import annotations + +import os +import tempfile +import time + +import numpy as np + +from src.types import EngineResult + +from .engine import SSTGNNEngine + + +class SSTGNNDetector(SSTGNNEngine): + threshold = 0.5 + + def detect_bytes(self, video_bytes: bytes) -> EngineResult: + frames = self._extract_video_frames(video_bytes) + if not frames: + return self._error_result(0.0, "No frames") + try: + return self.run_video(frames) + except Exception as exc: + return self._error_result(0.0, str(exc)) + + def _extract_video_frames(self, video_bytes: bytes) -> list: + try: + import cv2 + except Exception: + return [] + + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp: + tmp.write(video_bytes) + tmp_path = tmp.name + + frames = [] + try: + cap = cv2.VideoCapture(tmp_path) + index = 0 + while True: + ok, frame = cap.read() + if not ok: + break + if index % 4 == 0: + frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + index += 1 + if len(frames) >= 96: + break + cap.release() + return frames + finally: + os.unlink(tmp_path) + + def _detect_heuristic(self, landmark_seq: np.ndarray, t0: float) -> EngineResult: + velocities = np.diff(landmark_seq[:, :, :2], axis=0) + vel_std = float(np.std(velocities)) + anomaly = float(np.clip(1.0 - min(vel_std * 10.0, 1.0), 0.0, 1.0)) + is_fake = anomaly > self.threshold + confidence = anomaly if is_fake else (1.0 - anomaly) + + return EngineResult( + engine="sstgnn", + verdict="FAKE" if is_fake else "REAL", + confidence=confidence, + attributed_generator=None, + explanation=f"Heuristic landmark variance {vel_std:.4f}.", + processing_time_ms=max(0.0, (time.perf_counter() - t0) * 1000), + ) + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="sstgnn", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="N/A - SSTGNN requires video input.", + processing_time_ms=0.0, + ) + + @staticmethod + def _error_result(elapsed_s: float, reason: str = "") -> EngineResult: + return EngineResult( + engine="sstgnn", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation=f"SSTGNN engine unavailable ({reason}) - result is inconclusive.", + processing_time_ms=elapsed_s * 1000.0, + ) diff --git a/src/engines/sstgnn/engine.py b/src/engines/sstgnn/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..54c87b17ac6999201d762b651f4e9b3ec3b0373c --- /dev/null +++ b/src/engines/sstgnn/engine.py @@ -0,0 +1,293 @@ +from __future__ import annotations + +import logging +import os +import threading +import time +import urllib.request +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image + +from src.types import EngineResult + +logger = logging.getLogger(__name__) +CACHE = os.environ.get("MODEL_CACHE_DIR", "/tmp/models") + +_lock = threading.Lock() +_load_attempted = False +_det1 = None +_det2 = None +_mesh = None +_delaunay = None + + +def _skip_model_loads() -> bool: + return os.environ.get("GENAI_SKIP_MODEL_LOAD", "").strip().lower() in { + "1", + "true", + "yes", + "on", + } + + +def _get_pipeline(): + try: + from transformers import pipeline as hf_pipeline # type: ignore + except Exception: + from transformers.pipelines import pipeline as hf_pipeline # type: ignore + return hf_pipeline + + +KEYPOINT_STEP = 7 +KEYPOINT_COUNT = 68 + + +class _TasksFaceMeshAdapter: + def __init__(self, mp_module, landmarker) -> None: + self._mp = mp_module + self._landmarker = landmarker + + def process(self, frame: np.ndarray): + mp_image = self._mp.Image(image_format=self._mp.ImageFormat.SRGB, data=frame) + result = self._landmarker.detect(mp_image) + + class _Face: + def __init__(self, landmarks): + self.landmark = landmarks + + class _Result: + def __init__(self, faces): + self.multi_face_landmarks = faces + + faces = [_Face(landmarks) for landmarks in getattr(result, "face_landmarks", [])] + return _Result(faces) + + +def _ensure_face_landmarker_asset() -> Path: + cache_dir = Path(os.environ.get("MODEL_CACHE_DIR", "/tmp/models")) + model_path = cache_dir / "mediapipe" / "face_landmarker.task" + if model_path.exists(): + return model_path + + model_path.parent.mkdir(parents=True, exist_ok=True) + url = os.environ.get( + "MEDIAPIPE_FACE_LANDMARKER_URL", + ( + "https://storage.googleapis.com/mediapipe-models/face_landmarker/" + "face_landmarker/float16/latest/face_landmarker.task" + ), + ) + urllib.request.urlretrieve(url, model_path) + return model_path + + +def _build_image_classifier(model_id: str) -> Any: + pipeline = _get_pipeline() + + try: + return pipeline( + "image-classification", + model=model_id, + model_kwargs={"cache_dir": CACHE}, + ) + except Exception: + return pipeline("image-classification", model=model_id) + + +def _build_face_mesh() -> Any: + import mediapipe as mp # type: ignore + + if hasattr(mp, "solutions"): + return mp.solutions.face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + ) + + from mediapipe.tasks import python as mp_tasks_python # type: ignore + from mediapipe.tasks.python import vision # type: ignore + + model_path = _ensure_face_landmarker_asset() + options = vision.FaceLandmarkerOptions( + base_options=mp_tasks_python.BaseOptions(model_asset_path=str(model_path)), + running_mode=vision.RunningMode.IMAGE, + num_faces=1, + ) + landmarker = vision.FaceLandmarker.create_from_options(options) + return _TasksFaceMeshAdapter(mp, landmarker) + + +def _load() -> None: + global _det1, _det2, _mesh, _delaunay, _load_attempted + if _load_attempted: + return + + _load_attempted = True + if _skip_model_loads(): + logger.info("Skipping SSTGNN model load (GENAI_SKIP_MODEL_LOAD=1)") + return + + logger.info("Loading SSTGNN models...") + + try: + _det1 = _build_image_classifier("dima806/deepfake_vs_real_image_detection") + try: + _det2 = _build_image_classifier("prithivMLmods/Deep-Fake-Detector-Model") + except Exception: + logger.warning("SSTGNN backup detector unavailable") + except Exception as exc: + logger.warning("SSTGNN HF detector load failed: %s", exc) + + try: + _mesh = _build_face_mesh() + except Exception as exc: + logger.warning("MediaPipe FaceMesh unavailable for SSTGNN: %s", exc) + + try: + from scipy.spatial import Delaunay # type: ignore + + _delaunay = Delaunay + except Exception: + _delaunay = None + + logger.info("SSTGNN model load attempt complete") + + +def _fake_prob(preds: list[dict]) -> float: + fake_keywords = ("fake", "deepfake", "artificial", "generated", "ai", "synthetic") + best = 0.0 + for pred in preds: + label = str(pred.get("label", "")).lower() + score = float(pred.get("score", 0.0)) + if any(keyword in label for keyword in fake_keywords): + best = max(best, score) + if best == 0.0: + return 0.5 + return float(np.clip(best, 0.0, 1.0)) + + +class SSTGNNEngine: + def _ensure(self) -> None: + with _lock: + _load() + + def run(self, image: Image.Image) -> EngineResult: + t0 = time.perf_counter() + self._ensure() + + if image.mode != "RGB": + image = image.convert("RGB") + + scores: list[float] = [] + try: + if _det1 is not None: + scores.append(_fake_prob(_det1(image)) * 0.6) + except Exception as exc: + logger.warning("SSTGNN det1 error: %s", exc) + + if _det2 is not None: + try: + scores.append(_fake_prob(_det2(image)) * 0.4) + except Exception as exc: + logger.warning("SSTGNN det2 error: %s", exc) + + if not scores: + return EngineResult( + engine="sstgnn", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="All detectors failed; returning neutral score.", + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + cnn = sum(scores) / (0.6 if len(scores) == 1 else 1.0) + graph = self._geometry_score(np.array(image)) + final = float(np.clip(cnn * 0.7 + graph * 0.3, 0.0, 1.0)) + + return EngineResult( + engine="sstgnn", + verdict="FAKE" if final > 0.5 else "REAL", + confidence=final, + attributed_generator=None, + explanation=f"CNN {cnn:.2f}, geometric graph anomaly {graph:.2f}.", + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + def _geometry_score(self, frame: np.ndarray) -> float: + if _mesh is None: + return 0.3 + + try: + res = _mesh.process(frame) + if not res.multi_face_landmarks: + return 0.3 + + h, w = frame.shape[:2] + landmarks = res.multi_face_landmarks[0].landmark + idxs = list(range(0, 468, KEYPOINT_STEP))[:KEYPOINT_COUNT] + pts = np.array( + [[landmarks[i].x * w, landmarks[i].y * h] for i in idxs], + dtype=np.float32, + ) + + if _delaunay is None: + return 0.3 + + tri = _delaunay(pts) + areas = [] + for simplex in tri.simplices: + a, b, c = pts[simplex] + areas.append(abs(np.cross(b - a, c - a)) / 2.0) + + if not areas: + return 0.3 + + arr = np.array(areas, dtype=np.float32) + cv_score = float(np.std(arr) / (np.mean(arr) + 1e-9)) + return float(np.clip((cv_score - 0.8) / 1.5, 0.0, 1.0)) + + except Exception as exc: + logger.warning("Geometry score error: %s", exc) + return 0.3 + + def run_video(self, frames: list[np.ndarray]) -> EngineResult: + t0 = time.perf_counter() + self._ensure() + + if not frames: + return EngineResult( + engine="sstgnn", + verdict="REAL", + confidence=0.5, + attributed_generator=None, + explanation="No frames.", + processing_time_ms=0.0, + ) + + sample = frames[::6] or [frames[0]] + results = [self.run(Image.fromarray(frame)) for frame in sample] + avg = float(np.mean([r.confidence for r in results])) + + return EngineResult( + engine="sstgnn", + verdict="FAKE" if avg > 0.5 else "REAL", + confidence=avg, + attributed_generator=None, + explanation=f"Frame-sampled SSTGNN average {avg:.2f} over {len(sample)} frames.", + processing_time_ms=(time.perf_counter() - t0) * 1000, + ) + + @staticmethod + def image_stub() -> EngineResult: + return EngineResult( + engine="sstgnn", + verdict="UNKNOWN", + confidence=0.5, + attributed_generator=None, + explanation="N/A - SSTGNN requires video input.", + processing_time_ms=0.0, + ) diff --git a/src/engines/sstgnn/graph_builder.py b/src/engines/sstgnn/graph_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..0355b02d560c70424ef5698c577b144a75a3534f --- /dev/null +++ b/src/engines/sstgnn/graph_builder.py @@ -0,0 +1,106 @@ +""" +src/engines/sstgnn/graph_builder.py + +Graph construction for SSTGNN inference. +This is a direct copy of training/phase3_sstgnn/build_graph.py. +Keep both files in sync if the graph spec changes. +""" +from __future__ import annotations + +import math + +import numpy as np +import torch + +try: + from torch_geometric.data import Data + _TORCH_GEOMETRIC_AVAILABLE = True +except ImportError: + _TORCH_GEOMETRIC_AVAILABLE = False + + +def _build_adjacency_list(): + edges = [] + edges += [(i, i + 1) for i in range(16)] # jaw + edges += [(i, i + 1) for i in range(17, 21)] # right eyebrow + edges += [(i, i + 1) for i in range(22, 26)] # left eyebrow + edges += [(i, i + 1) for i in range(27, 30)] # nose bridge + edges += [(i, i + 1) for i in range(31, 35)] # nose base + edges += [(30, 35)] + edges += [(36,37),(37,38),(38,39),(39,40),(40,41),(41,36)] # right eye + edges += [(42,43),(43,44),(44,45),(45,46),(46,47),(47,42)] # left eye + edges += [(i, i+1) for i in range(48, 59)] + [(59, 48)] # mouth outer + edges += [(i, i+1) for i in range(60, 67)] + [(67, 60)] # mouth inner + return edges + + +SPATIAL_EDGES = _build_adjacency_list() +TEMPORAL_WINDOW = 3 + + +def build_temporal_graph(landmark_sequence: np.ndarray, label: int = -1): + """ + Convert (T, L, 3) landmark sequence to torch_geometric Data. + + Args: + landmark_sequence: (T, L, 3) float array, xyz in [0,1] + label: binary label (-1 for inference) + + Returns: + torch_geometric.data.Data + """ + if not _TORCH_GEOMETRIC_AVAILABLE: + raise ImportError( + "torch_geometric is required for SSTGNN inference. " + "Install per KAGGLE.md Cell 1." + ) + + T, L, _ = landmark_sequence.shape + + # Node features: (x, y, z, frame_t/T, landmark_idx/L) + node_feats = [] + for t in range(T): + for l in range(L): + x, y, z = landmark_sequence[t, l] + node_feats.append([ + float(x), float(y), float(z), + t / max(T - 1, 1), + l / max(L - 1, 1), + ]) + + node_tensor = torch.tensor(node_feats, dtype=torch.float32) + + src_nodes, dst_nodes, weights = [], [], [] + + def nid(t, l): + return t * L + l + + # Spatial edges + for t in range(T): + for i, j in SPATIAL_EDGES: + if i < L and j < L: + u, v = nid(t, i), nid(t, j) + src_nodes += [u, v]; dst_nodes += [v, u]; weights += [1.0, 1.0] + + # Temporal edges + for t in range(T): + for dt in range(1, TEMPORAL_WINDOW + 1): + t2 = t + dt + if t2 >= T: + break + w = math.exp(-dt) + for l in range(L): + u, v = nid(t, l), nid(t2, l) + src_nodes += [u, v]; dst_nodes += [v, u]; weights += [w, w] + + # Self edges + for n in range(T * L): + src_nodes.append(n); dst_nodes.append(n); weights.append(1.0) + + edge_index = torch.tensor([src_nodes, dst_nodes], dtype=torch.long) + edge_attr = torch.tensor(weights, dtype=torch.float32).unsqueeze(1) + + data = Data(x=node_tensor, edge_index=edge_index, edge_attr=edge_attr) + if label >= 0: + data.y = torch.tensor([label], dtype=torch.float32) + return data diff --git a/src/explainability/__init__.py b/src/explainability/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/explainability/explainer.py b/src/explainability/explainer.py new file mode 100644 index 0000000000000000000000000000000000000000..1ed51eadb10d7288a242bd05b3bf1bb828b11b05 --- /dev/null +++ b/src/explainability/explainer.py @@ -0,0 +1,288 @@ +from __future__ import annotations + +import logging +import os +import queue +import threading + +from src.types import DetectionResponse, EngineResult + +logger = logging.getLogger(__name__) + +try: + from google import genai as genai_new # type: ignore +except Exception: + genai_new = None + +genai_legacy = None + + +SYSTEM_INSTRUCTION = ( + "You are a deepfake forensics analyst writing reports for security professionals. " + "Given detection engine outputs, write exactly 2-3 sentences in plain English " + "explaining why the content is real or fake. " + "Be specific and name the strongest signals. " + "Use direct declarative sentences. " + "Output only the explanation text." +) + +MODEL_CANDIDATES = ( + "gemini-2.5-pro", + "gemini-2.5-pro-latest", + "gemini-2.0-flash", + "gemini-1.5-pro", + "gemini-1.5-pro-latest", + # legacy names kept as last-resort candidates + "gemini-2.5-pro-preview-03-25", + "gemini-1.5-pro-002", +) + +REQUEST_TIMEOUT_S = float(os.environ.get("GEMINI_REQUEST_TIMEOUT_S", "10")) +MAX_MODEL_ATTEMPTS = max(1, int(os.environ.get("GEMINI_MAX_MODEL_ATTEMPTS", "3"))) +ENABLE_LEGACY_MODEL_DISCOVERY = os.environ.get("GEMINI_DISCOVER_MODELS", "").strip().lower() in { + "1", + "true", + "yes", + "on", +} + +_new_client = None +_legacy_model = None +_legacy_model_name = None +_legacy_candidates = None + + +def _get_api_key() -> str: + return os.environ.get("GEMINI_API_KEY", "").strip() + + +def _run_with_timeout(func, timeout_s: float): + result_q: queue.Queue[tuple[bool, object]] = queue.Queue(maxsize=1) + + def _runner() -> None: + try: + result_q.put((True, func())) + except Exception as exc: # pragma: no cover - passthrough + result_q.put((False, exc)) + + thread = threading.Thread(target=_runner, daemon=True) + thread.start() + + try: + ok, payload = result_q.get(timeout=timeout_s) + except queue.Empty as exc: + raise TimeoutError(f"Gemini request timed out after {timeout_s:.1f}s") from exc + + if ok: + return payload + raise payload # type: ignore[misc] + + +def _ensure_new_client(): + global _new_client + if _new_client is not None: + return _new_client + if genai_new is None: + return None + + api_key = _get_api_key() + if not api_key: + return None + + try: + _new_client = genai_new.Client(api_key=api_key) + return _new_client + except Exception as exc: + logger.warning("Failed to init google.genai client: %s", exc) + return None + + +def _generate_with_new_sdk(prompt: str) -> str: + client = _ensure_new_client() + if client is None: + raise RuntimeError("google.genai client unavailable") + + full_prompt = f"{SYSTEM_INSTRUCTION}\n\n{prompt}" + last_error: Exception | None = None + + for model_name in MODEL_CANDIDATES: + try: + response = _run_with_timeout( + lambda: client.models.generate_content( + model=model_name, + contents=full_prompt, + ), + REQUEST_TIMEOUT_S, + ) + text = getattr(response, "text", None) + if text and str(text).strip(): + logger.info("Gemini explain model selected (new SDK): %s", model_name) + return str(text).strip() + except Exception as exc: + last_error = exc + logger.debug("Gemini model %s failed on new SDK: %s", model_name, exc) + + if last_error: + raise last_error + raise RuntimeError("No Gemini model succeeded via new SDK") + + +def _ensure_legacy_configured() -> bool: + global genai_legacy + if genai_legacy is None: + try: + import google.generativeai as _legacy # type: ignore + genai_legacy = _legacy + except Exception: + return False + + if genai_legacy is None: + return False + api_key = _get_api_key() + if not api_key: + return False + + try: + genai_legacy.configure(api_key=api_key) + return True + except Exception as exc: + logger.warning("Failed to configure legacy Gemini SDK: %s", exc) + return False + + +def _legacy_model_candidates() -> tuple[str, ...]: + global _legacy_candidates + + if _legacy_candidates is not None: + return _legacy_candidates + + ordered = list(MODEL_CANDIDATES) + if not ENABLE_LEGACY_MODEL_DISCOVERY: + _legacy_candidates = tuple(ordered) + return _legacy_candidates + + if genai_legacy is None: + _legacy_candidates = tuple(ordered) + return _legacy_candidates + + try: + discovered: list[str] = [] + for model in genai_legacy.list_models(request_options={"timeout": REQUEST_TIMEOUT_S}): + methods = set(getattr(model, "supported_generation_methods", []) or []) + if "generateContent" not in methods: + continue + name = str(getattr(model, "name", "")).strip() + if not name: + continue + short = name.split("/", 1)[-1] + discovered.append(short) + + if discovered: + preferred = [name for name in ordered if name in discovered] + remainder = [name for name in discovered if name not in preferred] + _legacy_candidates = tuple(preferred + remainder) + else: + _legacy_candidates = tuple(ordered) + except Exception as exc: + logger.warning("Could not list Gemini models from legacy SDK: %s", exc) + _legacy_candidates = tuple(ordered) + + return _legacy_candidates + + +def _generate_with_legacy_sdk(prompt: str) -> str: + global _legacy_model, _legacy_model_name + + if not _ensure_legacy_configured(): + raise RuntimeError("legacy Gemini SDK unavailable") + + if _legacy_model is not None: + try: + response = _run_with_timeout( + lambda: _legacy_model.generate_content( + prompt, + request_options={"timeout": REQUEST_TIMEOUT_S}, + ), + REQUEST_TIMEOUT_S + 1.0, + ) + text = (getattr(response, "text", None) or "").strip() + if text: + return text + except Exception as exc: + logger.warning("Cached Gemini model %s failed: %s", _legacy_model_name, exc) + _legacy_model = None + _legacy_model_name = None + + last_error: Exception | None = None + for model_name in _legacy_model_candidates()[:MAX_MODEL_ATTEMPTS]: + try: + candidate = genai_legacy.GenerativeModel( + model_name=model_name, + system_instruction=SYSTEM_INSTRUCTION, + ) + response = _run_with_timeout( + lambda: candidate.generate_content( + prompt, + request_options={"timeout": REQUEST_TIMEOUT_S}, + ), + REQUEST_TIMEOUT_S + 1.0, + ) + text = (getattr(response, "text", None) or "").strip() + if text: + _legacy_model = candidate + _legacy_model_name = model_name + logger.info("Gemini explain model selected (legacy SDK): %s", model_name) + return text + except Exception as exc: + last_error = exc + logger.debug("Gemini model %s failed on legacy SDK: %s", model_name, exc) + + if last_error: + raise last_error + raise RuntimeError("No Gemini model succeeded via legacy SDK") + + +def explain( + verdict: str, + confidence: float, + engine_results: list[EngineResult], + generator: str, +) -> str: + breakdown = "\n".join( + f"- {result.engine}: {result.verdict} ({result.confidence:.0%}) - {result.explanation}" + for result in engine_results + ) + + prompt = ( + f"Verdict: {verdict} ({confidence:.0%} confidence)\n" + f"Attributed generator: {generator}\n" + f"Engine breakdown:\n{breakdown}\n\n" + "Write the forensics explanation." + ) + + try: + if genai_new is not None: + return _generate_with_new_sdk(prompt) + return _generate_with_legacy_sdk(prompt) + + except Exception as exc: + logger.error("Gemini explain failed: %s", exc) + top = engine_results[0] if engine_results else None + primary = f"Primary signal came from the {top.engine} engine." if top else "" + return ( + f"Content classified as {verdict} with {confidence:.0%} confidence. " + f"Attributed generator: {generator}. " + f"{primary}" + ).strip() + + +class Explainer: + """Compatibility wrapper for legacy callers expecting an object API.""" + + def explain(self, response: DetectionResponse) -> str: + return explain( + verdict=response.verdict, + confidence=response.confidence, + engine_results=response.engine_breakdown, + generator=response.attributed_generator, + ) diff --git a/src/fusion/__init__.py b/src/fusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/fusion/fuser.py b/src/fusion/fuser.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0a5cc77ee669a59c059ef6b2bba39987558373 --- /dev/null +++ b/src/fusion/fuser.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import numpy as np + +from src.types import DetectionResponse, EngineResult + +ENGINE_WEIGHTS = { + "fingerprint": 0.45, + "coherence": 0.35, + "sstgnn": 0.20, +} + +ENGINE_WEIGHTS_VIDEO = { + "fingerprint": 0.30, + "coherence": 0.50, + "sstgnn": 0.20, +} + +ATTRIBUTION_PRIORITY = { + "fingerprint": 1, + "sstgnn": 2, + "coherence": 3, +} + + +def _normalize_generator(value: str | None) -> str: + if not value: + return "real" + return str(value).strip().lower().replace(" ", "_") + + +def fuse(results: list[EngineResult], is_video: bool = False) -> tuple[str, float, str]: + """Return (verdict, confidence_for_verdict, attributed_generator).""" + + weights = ENGINE_WEIGHTS_VIDEO if is_video else ENGINE_WEIGHTS + active = [result for result in results if result.verdict != "UNKNOWN"] + + if not active: + return "UNKNOWN", 0.5, "unknown_gan" + + wf = sum( + result.confidence * weights.get(result.engine, 0.1) + for result in active + if result.verdict == "FAKE" + ) + wr = sum( + (1.0 - result.confidence) * weights.get(result.engine, 0.1) + for result in active + if result.verdict == "REAL" + ) + + denom = wf + wr + 1e-9 + fake_prob = float(np.clip(wf / denom, 0.0, 1.0)) + verdict = "FAKE" if fake_prob > 0.5 else "REAL" + confidence = fake_prob if verdict == "FAKE" else (1.0 - fake_prob) + + generator = "real" + if verdict == "FAKE": + for result in sorted(active, key=lambda r: ATTRIBUTION_PRIORITY.get(r.engine, 9)): + candidate = _normalize_generator(result.attributed_generator) + if candidate and candidate != "real": + generator = candidate + break + if generator == "real": + generator = "unknown_gan" + + return verdict, confidence, generator + + +class Fuser: + """Compatibility wrapper returning `DetectionResponse` objects.""" + + def fuse( + self, + results: list[EngineResult], + media_type: str = "image", + total_ms: float = 0.0, + ) -> DetectionResponse: + if not results: + return DetectionResponse( + verdict="REAL", + confidence=0.5, + attributed_generator="unknown_gan", + explanation="No engine results available.", + processing_time_ms=round(total_ms, 2), + engine_breakdown=[], + ) + + verdict, confidence, generator = fuse(results, is_video=(media_type == "video")) + + if verdict == "UNKNOWN": + explanation = "No active engine outputs were available." + else: + summary = ", ".join( + f"{result.engine}:{result.verdict}({result.confidence:.2f})" + for result in results + ) + explanation = f"Fused {media_type} analysis from engines: {summary}." + + return DetectionResponse( + verdict=verdict, + confidence=confidence, + attributed_generator=generator, + explanation=explanation, + processing_time_ms=round(total_ms, 2), + engine_breakdown=results, + ) diff --git a/src/services/__init__.py b/src/services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b4335581ad5af85d90f52c6ecd47cec338fdae --- /dev/null +++ b/src/services/__init__.py @@ -0,0 +1 @@ +# src/services — hosting / inference routing layer diff --git a/src/services/hf_inference_client.py b/src/services/hf_inference_client.py new file mode 100644 index 0000000000000000000000000000000000000000..b620311d8c2c9412a7f66cfe3bf203c3f011acbb --- /dev/null +++ b/src/services/hf_inference_client.py @@ -0,0 +1,126 @@ +""" +src/services/hf_inference_client.py — HuggingFace Serverless Inference API + +Free-tier image classification and feature extraction via +https://api-inference.huggingface.co/models/{model_id} + +No GPU required on our side; HF runs inference on shared hardware. +Rate limits: ~1000 requests/day on the free tier. + +Usage: + client = HFInferenceClient() + results = await client.classify_image(image_bytes) +""" +from __future__ import annotations + +import logging +import os +from typing import Any + +import httpx + +logger = logging.getLogger(__name__) + +_HF_API_BASE = "https://api-inference.huggingface.co" +_DEFAULT_MODEL = "Wvolf/ViT_Deepfake_Detection" + + +class HFInferenceUnavailable(RuntimeError): + """Raised when the HF Inference API is unreachable or returns an error.""" + + +class HFInferenceClient: + """ + Wraps the HuggingFace Serverless Inference REST API. + + Environment variables + --------------------- + HF_TOKEN — HuggingFace access token (required for private models, + recommended for higher rate limits on public ones) + """ + + def __init__(self, model_id: str = _DEFAULT_MODEL) -> None: + self._model_id = model_id + self._token = ( + os.environ.get("HF_TOKEN") + or os.environ.get("HuggingFaceToken") + or "" + ) + + @property + def available(self) -> bool: + return True # Public endpoint works without a token (lower rate limit) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + async def classify_image( + self, + image_bytes: bytes, + timeout: float = 30.0, + ) -> list[dict[str, Any]]: + """ + Submit *image_bytes* for image classification. + + Returns list of dicts, e.g.: + [{"label": "FAKE", "score": 0.95}, {"label": "REAL", "score": 0.05}] + + Raises HFInferenceUnavailable on non-2xx responses. + """ + url = f"{_HF_API_BASE}/models/{self._model_id}" + headers = self._auth_headers() + headers["Content-Type"] = "application/octet-stream" + + async with httpx.AsyncClient(timeout=timeout) as client: + resp = await client.post(url, content=image_bytes, headers=headers) + + if resp.status_code == 503: + # Model is loading — common on cold start + logger.warning("HF model loading (503); you can retry in ~20s") + raise HFInferenceUnavailable( + f"HF model {self._model_id} is loading (503). Retry shortly." + ) + if not resp.is_success: + raise HFInferenceUnavailable( + f"HF Inference API returned {resp.status_code}: {resp.text[:200]}" + ) + + data = resp.json() + logger.debug("HF classify response: %s", data) + return data # type: ignore[return-value] + + async def get_features( + self, + image_bytes: bytes, + timeout: float = 30.0, + ) -> list[float]: + """ + Extract image feature embeddings via the /feature-extraction pipeline. + Returns a flat list of float values. + """ + url = f"{_HF_API_BASE}/pipeline/feature-extraction/{self._model_id}" + headers = self._auth_headers() + headers["Content-Type"] = "application/octet-stream" + + async with httpx.AsyncClient(timeout=timeout) as client: + resp = await client.post(url, content=image_bytes, headers=headers) + + if not resp.is_success: + raise HFInferenceUnavailable( + f"HF feature-extraction returned {resp.status_code}: {resp.text[:200]}" + ) + data = resp.json() + # HF returns nested list for single image — flatten one level + if isinstance(data, list) and data and isinstance(data[0], list): + return data[0] + return data # type: ignore[return-value] + + # ------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------ + + def _auth_headers(self) -> dict[str, str]: + if self._token: + return {"Authorization": f"Bearer {self._token}"} + return {} diff --git a/src/services/inference_router.py b/src/services/inference_router.py new file mode 100644 index 0000000000000000000000000000000000000000..051355fbde26310c0368b5d143c530ef0f555d8d --- /dev/null +++ b/src/services/inference_router.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import base64 +import logging +import os +from typing import Literal + +import httpx + +from src.types import DetectionResponse + +logger = logging.getLogger(__name__) +InferenceBackend = Literal["auto", "local", "runpod", "hf"] + + +def _is_placeholder(value: str) -> bool: + v = value.strip().lower() + return ( + not v + or "your_serverless_endpoint_id_here" in v + or "replace_me" in v + or "example" in v + ) + + +def get_inference_backend() -> InferenceBackend: + """ + Resolve backend mode from INFERENCE_BACKEND. + + Supported values: + - auto : local by default, route large videos to RunPod when configured + - local : always local + - runpod : always try RunPod first, then fallback in caller + - hf : Hugging Face first, then RunPod fallback (no local fallback) + """ + value = os.environ.get("INFERENCE_BACKEND", "auto").strip().lower() + if value in {"auto", "local", "runpod", "hf"}: + return value # type: ignore[return-value] + + logger.warning( + "Invalid INFERENCE_BACKEND=%r; falling back to 'auto'. " + "Expected one of: auto, local, runpod, hf.", + value, + ) + return "auto" + + +def is_runpod_configured() -> bool: + key = os.environ.get("RUNPOD_API_KEY", "").strip() + endpoint = ( + os.environ.get("RUNPOD_ENDPOINT_ID") + or os.environ.get("RUNPOD_ENDPOINT") + or "" + ).strip() + return bool(key) and not _is_placeholder(endpoint) + + +async def route_inference(data: bytes, media_type: str) -> DetectionResponse: + """Send payload to RunPod Serverless runsync endpoint.""" + + runpod_key = os.environ.get("RUNPOD_API_KEY", "").strip() + runpod_endpoint = ( + os.environ.get("RUNPOD_ENDPOINT_ID") + or os.environ.get("RUNPOD_ENDPOINT") + or "" + ).strip() + + if not is_runpod_configured(): + raise RuntimeError( + "RunPod not configured. Set valid RUNPOD_API_KEY and RUNPOD_ENDPOINT_ID." + ) + + url = f"https://api.runpod.ai/v2/{runpod_endpoint}/runsync" + payload = { + "input": { + "data": base64.b64encode(data).decode("utf-8"), + "media_type": media_type, + } + } + + headers = { + "Authorization": f"Bearer {runpod_key}", + "Content-Type": "application/json", + } + + async with httpx.AsyncClient(timeout=120.0) as client: + response = await client.post(url, json=payload, headers=headers) + response.raise_for_status() + + body = response.json() + output = body.get("output") + if output is None: + raise RuntimeError(f"RunPod response missing output: {body}") + + return DetectionResponse(**output) + + +class InferenceRouter: + """Compatibility wrapper for older router callers.""" + + async def route(self, data: bytes, media_type: str) -> DetectionResponse: + return await route_inference(data, media_type) diff --git a/src/services/runpod_client.py b/src/services/runpod_client.py new file mode 100644 index 0000000000000000000000000000000000000000..cc8bb098d4ee3c37aa7ebf67b22a5300d13ee175 --- /dev/null +++ b/src/services/runpod_client.py @@ -0,0 +1,132 @@ +""" +src/services/runpod_client.py — RunPod Serverless inference client + +Submits image/frame payloads to a RunPod Serverless endpoint and polls for +results. Falls back gracefully when RUNPOD_API_KEY or RUNPOD_ENDPOINT_ID +are unset. + +Usage: + client = RunPodClient() + result = await client.classify_image(image_bytes) +""" +from __future__ import annotations + +import asyncio +import base64 +import logging +import os +import time +from typing import Any, Optional + +import httpx + +logger = logging.getLogger(__name__) + +_API_BASE = "https://api.runpod.ai/v2" +_POLL_INTERVAL_SEC = 1.0 +_MAX_POLL_SECONDS = 55 # Keep under typical 60-s Lambda timeout + + +class RunPodUnavailable(RuntimeError): + """Raised when RunPod is not configured or returns a non-retryable error.""" + + +class RunPodClient: + """ + Thin async client for RunPod Serverless endpoints. + + Environment variables + --------------------- + RUNPOD_API_KEY — RunPod API key (rpa_…) + RUNPOD_ENDPOINT_ID — ID of the deployed serverless endpoint + RUNPOD_MODEL_ID — HF model ID used inside the RunPod worker (informational) + """ + + def __init__(self) -> None: + self._api_key = os.environ.get("RUNPOD_API_KEY", "") + self._endpoint_id = os.environ.get("RUNPOD_ENDPOINT_ID", "") + self._model_id = os.environ.get("RUNPOD_MODEL_ID", "Wvolf/ViT_Deepfake_Detection") + + @property + def available(self) -> bool: + """True when both API key and endpoint ID are configured.""" + return bool(self._api_key and self._endpoint_id) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + async def classify_image( + self, + image_bytes: bytes, + timeout: float = _MAX_POLL_SECONDS, + ) -> dict[str, Any]: + """ + Send *image_bytes* to the RunPod endpoint and return the worker result. + + Returns a dict like: + {"verdict": "FAKE", "confidence": 0.95, "generator": "stable_diffusion"} + + Raises RunPodUnavailable if not configured or on persistent failure. + """ + if not self.available: + raise RunPodUnavailable( + "RunPod not configured: set RUNPOD_API_KEY and RUNPOD_ENDPOINT_ID" + ) + + b64 = base64.b64encode(image_bytes).decode() + payload = { + "input": { + "image_b64": b64, + "model_id": self._model_id, + } + } + + job_id = await self._submit(payload) + logger.info("RunPod job submitted: %s", job_id) + return await self._poll(job_id, timeout=timeout) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + async def _submit(self, payload: dict[str, Any]) -> str: + url = f"{_API_BASE}/{self._endpoint_id}/run" + headers = { + "Authorization": f"Bearer {self._api_key}", + "Content-Type": "application/json", + } + async with httpx.AsyncClient(timeout=30) as client: + resp = await client.post(url, json=payload, headers=headers) + resp.raise_for_status() + data = resp.json() + if "id" not in data: + raise RunPodUnavailable(f"Unexpected RunPod response: {data}") + return data["id"] + + async def _poll(self, job_id: str, timeout: float) -> dict[str, Any]: + url = f"{_API_BASE}/{self._endpoint_id}/status/{job_id}" + headers = {"Authorization": f"Bearer {self._api_key}"} + deadline = time.monotonic() + timeout + + async with httpx.AsyncClient(timeout=10) as client: + while time.monotonic() < deadline: + resp = await client.get(url, headers=headers) + resp.raise_for_status() + data = resp.json() + status = data.get("status", "") + + if status == "COMPLETED": + output = data.get("output") or {} + logger.info("RunPod job %s completed: %s", job_id, output) + return output # type: ignore[return-value] + + if status in ("FAILED", "CANCELLED"): + err = data.get("error", "unknown RunPod error") + raise RunPodUnavailable(f"RunPod job {job_id} {status}: {err}") + + await asyncio.sleep(_POLL_INTERVAL_SEC) + + raise RunPodUnavailable( + f"RunPod job {job_id} did not complete within {timeout}s" + ) diff --git a/src/training/__init__.py b/src/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/training/config.py b/src/training/config.py new file mode 100644 index 0000000000000000000000000000000000000000..eb2bbe1bf86f7f13bd0da4cbff211826011ab40c --- /dev/null +++ b/src/training/config.py @@ -0,0 +1,150 @@ +""" +src/training/config.py — TrainingConfig dataclass + +Single source of truth for all training hyperparameters. Import this from +every training script instead of duplicating argparse defaults. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import List + + +# Generator label index mapping — must match GeneratorLabel enum in src/types.py +# and the classification head in every model file. +GENERATOR_CLASSES: List[str] = [ + "real", # 0 + "unknown_gan", # 1 + "stable_diffusion", # 2 + "midjourney", # 3 + "dall_e", # 4 + "flux", # 5 + "firefly", # 6 + "imagen", # 7 +] +NUM_GENERATOR_CLASSES: int = len(GENERATOR_CLASSES) # 8 — never change this + + +@dataclass +class TrainingConfig: + """Fingerprint engine training configuration (Phase 1).""" + + # ── Paths ───────────────────────────────────────────────────────────────── + data_dir: Path = Path("data/processed/fingerprint") + output_dir: Path = Path("models/checkpoints/fingerprint") + log_dir: Path = Path("training/logs") + + # ── Model ───────────────────────────────────────────────────────────────── + model_name: str = "vit_base_patch16_224" # timm slug + pretrained: bool = True + num_binary_classes: int = 2 + num_generator_classes: int = NUM_GENERATOR_CLASSES # 8 + + # ── Training ────────────────────────────────────────────────────────────── + epochs: int = 30 + batch_size: int = 64 + learning_rate: float = 2e-5 + weight_decay: float = 0.01 + warmup_steps: int = 500 + grad_accumulation: int = 1 + amp: bool = True + generator_loss_weight: float = 0.3 # secondary objective weight + + # ── Optimiser ───────────────────────────────────────────────────────────── + optimizer: str = "adamw" + scheduler: str = "cosine" + + # ── Early stopping ──────────────────────────────────────────────────────── + patience: int = 5 # stop if val AUC flat for N epochs + + # ── Reproducibility ─────────────────────────────────────────────────────── + seed: int = 42 + + # ── Kaggle ──────────────────────────────────────────────────────────────── + is_kaggle: bool = False + + def __post_init__(self) -> None: + self.data_dir = Path(self.data_dir) + self.output_dir = Path(self.output_dir) + self.log_dir = Path(self.log_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.log_dir.mkdir(parents=True, exist_ok=True) + + def to_dict(self) -> dict: + return { + k: str(v) if isinstance(v, Path) else v + for k, v in self.__dict__.items() + } + + +@dataclass +class CoherenceConfig: + """Coherence engine training configuration (Phase 2).""" + + data_dir: Path = Path("data/processed/coherence") + output_dir: Path = Path("models/checkpoints/coherence") + log_dir: Path = Path("training/logs") + + epochs: int = 25 + batch_size: int = 16 + learning_rate: float = 1e-4 + weight_decay: float = 1e-4 + contrastive_weight: float = 0.1 + amp: bool = True + patience: int = 5 + seed: int = 42 + is_kaggle: bool = False + + def __post_init__(self) -> None: + self.data_dir = Path(self.data_dir) + self.output_dir = Path(self.output_dir) + self.log_dir = Path(self.log_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.log_dir.mkdir(parents=True, exist_ok=True) + + def to_dict(self) -> dict: + return { + k: str(v) if isinstance(v, Path) else v + for k, v in self.__dict__.items() + } + + +@dataclass +class SSTGNNConfig: + """SSTGNN engine training configuration (Phase 3).""" + + data_dir: Path = Path("data/processed/sstgnn") + output_dir: Path = Path("models/checkpoints/sstgnn") + log_dir: Path = Path("training/logs") + + # Graph model + in_channels: int = 5 # node feature dim: x, y, z, frame_idx/T, lm_idx/L + hidden_dim: int = 64 + heads: int = 4 + num_gat_layers: int = 3 + + # Training — note: no AMP by default (NaN risk on GNNs) + epochs: int = 40 + batch_size: int = 8 + learning_rate: float = 5e-4 + weight_decay: float = 5e-4 + grad_clip: float = 1.0 # always clip for GNNs + amp: bool = False + patience: int = 8 # GNNs need longer to plateau + seed: int = 42 + is_kaggle: bool = False + + def __post_init__(self) -> None: + self.data_dir = Path(self.data_dir) + self.output_dir = Path(self.output_dir) + self.log_dir = Path(self.log_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.log_dir.mkdir(parents=True, exist_ok=True) + + def to_dict(self) -> dict: + return { + k: str(v) if isinstance(v, Path) else v + for k, v in self.__dict__.items() + } diff --git a/src/training/datasets.py b/src/training/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..2363e45b11b9222cc68c36a64635f4bb3038882a --- /dev/null +++ b/src/training/datasets.py @@ -0,0 +1,91 @@ +""" +src/training/datasets.py + +Shared dataset utilities used by scripts/ entrypoints. +""" +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Optional + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + +IMAGENET_MEAN = [0.485, 0.456, 0.406] +IMAGENET_STD = [0.229, 0.224, 0.225] + + +def get_train_transform(size: int = 224): + return transforms.Compose([ + transforms.RandomResizedCrop(size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(0.2, 0.2, 0.2, 0.1), + transforms.ToTensor(), + transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD), + ]) + + +def get_val_transform(size: int = 224): + return transforms.Compose([ + transforms.Resize(int(size * 256 / 224)), + transforms.CenterCrop(size), + transforms.ToTensor(), + transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD), + ]) + + +class ImageManifestDataset(Dataset): + """ + Generic image dataset driven by a manifest CSV. + + Manifest format: filepath, label (0=real, 1=fake), [generator (int)] + """ + + def __init__( + self, + manifest_path: Path, + transform=None, + root_dir: Optional[Path] = None, + ): + self.transform = transform + self.root_dir = Path(root_dir) if root_dir else None + self.samples = [] + + with open(manifest_path) as f: + reader = csv.DictReader(f) + for row in reader: + filepath = Path(row["filepath"]) + if self.root_dir and not filepath.is_absolute(): + filepath = self.root_dir / filepath + label = int(row["label"]) + generator = int(row.get("generator", 0)) + self.samples.append((filepath, label, generator)) + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int) -> dict: + path, label, generator = self.samples[idx] + img = Image.open(path).convert("RGB") + if self.transform: + img = self.transform(img) + return { + "image": img, + "label": label, + "generator": generator, + "filepath": str(path), + } + + def get_class_weights(self) -> torch.Tensor: + labels = [s[1] for s in self.samples] + n_real = labels.count(0) + n_fake = labels.count(1) + w_real = 1.0 / max(n_real, 1) + w_fake = 1.0 / max(n_fake, 1) + return torch.tensor([w_real, w_fake], dtype=torch.float32) diff --git a/src/training/image_trainer.py b/src/training/image_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..bac51bb7ff21e5e469203eaf9f641856b12b750b --- /dev/null +++ b/src/training/image_trainer.py @@ -0,0 +1,149 @@ +""" +src/training/image_trainer.py + +Reusable Trainer class wrapping the Fingerprint engine training loop. +Used by scripts/train_image_baseline.py. +""" +from __future__ import annotations + +import logging +import time +from pathlib import Path +from typing import Optional + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import GradScaler, autocast +from torch.utils.data import DataLoader +from sklearn.metrics import roc_auc_score, accuracy_score + +from src.training.config import TrainingConfig + +log = logging.getLogger(__name__) + + +class ImageTrainer: + """ + Encapsulates the training loop for image-based classification models. + + Usage: + trainer = ImageTrainer(model, config, train_loader, val_loader, device) + trainer.train() + """ + + def __init__( + self, + model: nn.Module, + config: TrainingConfig, + train_loader: DataLoader, + val_loader: DataLoader, + device: torch.device, + criterion: Optional[nn.Module] = None, + ): + self.model = model.to(device) + self.config = config + self.train_loader = train_loader + self.val_loader = val_loader + self.device = device + self.criterion = criterion or nn.CrossEntropyLoss() + + self.optimizer = torch.optim.AdamW( + model.parameters(), + lr=config.learning_rate, + weight_decay=config.weight_decay, + ) + self.scaler = GradScaler(enabled=config.amp) + + self.best_auc = 0.0 + self.no_improve = 0 + self.output_dir = Path(config.output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def train(self) -> float: + """Run full training. Returns best val AUC.""" + log.info(f"Training for {self.config.epochs} epochs on {self.device}") + + for epoch in range(self.config.epochs): + t0 = time.time() + train_loss = self._train_epoch(epoch) + val_metrics = self._validate() + + log.info( + f"Epoch {epoch+1:02d}/{self.config.epochs} | " + f"train_loss={train_loss:.4f} | " + f"val_auc={val_metrics['auc']:.4f} | " + f"val_acc={val_metrics['accuracy']:.4f} | " + f"{time.time()-t0:.0f}s" + ) + + self._save("latest.pt", epoch, val_metrics["auc"]) + + if val_metrics["auc"] > self.best_auc: + self.best_auc = val_metrics["auc"] + self.no_improve = 0 + self._save("best.pt", epoch, self.best_auc) + log.info(f" New best AUC: {self.best_auc:.4f}") + else: + self.no_improve += 1 + if self.no_improve >= self.config.patience: + log.info(f"Early stopping at epoch {epoch+1}") + break + + return self.best_auc + + def _train_epoch(self, epoch: int) -> float: + self.model.train() + total_loss = 0.0 + + for i, batch in enumerate(self.train_loader): + images = batch["image"].to(self.device) + labels = batch["label"].to(self.device) + + with autocast(enabled=self.config.amp): + outputs = self.model(images) + logits = outputs.get("binary_logits", outputs) if isinstance(outputs, dict) else outputs + loss = self.criterion(logits, labels) / self.config.grad_accumulation + + self.scaler.scale(loss).backward() + + if (i + 1) % self.config.grad_accumulation == 0: + self.scaler.unscale_(self.optimizer) + nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + total_loss += loss.item() * self.config.grad_accumulation + + return total_loss / len(self.train_loader) + + @torch.no_grad() + def _validate(self) -> dict: + self.model.eval() + all_probs, all_labels = [], [] + + for batch in self.val_loader: + images = batch["image"].to(self.device) + labels = batch["label"] + + outputs = self.model(images) + logits = outputs.get("binary_logits", outputs) if isinstance(outputs, dict) else outputs + probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy() + + all_probs.extend(probs) + all_labels.extend(labels.numpy()) + + auc = roc_auc_score(all_labels, all_probs) if len(set(all_labels)) > 1 else 0.5 + acc = accuracy_score(all_labels, (np.array(all_probs) > 0.5).astype(int)) + return {"auc": auc, "accuracy": acc} + + def _save(self, filename: str, epoch: int, val_auc: float) -> None: + torch.save({ + "epoch": epoch, + "model_state": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "scaler": self.scaler.state_dict(), + "val_auc": val_auc, + "config": self.config.to_dict(), + }, self.output_dir / filename) diff --git a/src/training/manifests.py b/src/training/manifests.py new file mode 100644 index 0000000000000000000000000000000000000000..60e1f7abb481af0942485c38d1f03cc0c65a8a6f --- /dev/null +++ b/src/training/manifests.py @@ -0,0 +1,76 @@ +"""src/training/manifests.py — Build and validate manifest CSV files.""" +from __future__ import annotations + +import csv +import random +from pathlib import Path +from typing import List, Tuple + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + + +def build_manifest( + image_dirs: List[Tuple[Path, int, int]], # (dir, label, generator_idx) + output_path: Path, + seed: int = 42, + train_ratio: float = 0.80, + val_ratio: float = 0.10, +) -> dict: + """ + Walk image directories, build split manifests. + + Returns dict with train/val/test paths. + """ + rng = random.Random(seed) + records = [] + + for img_dir, label, generator in image_dirs: + for p in sorted(Path(img_dir).rglob("*")): + if p.suffix.lower() in IMAGE_EXTS: + records.append({ + "filepath": str(p), + "label": label, + "generator": generator, + }) + + rng.shuffle(records) + n = len(records) + n_train = int(n * train_ratio) + n_val = int(n * val_ratio) + + splits = { + "train": records[:n_train], + "val": records[n_train:n_train + n_val], + "test": records[n_train + n_val:], + } + + output_path.parent.mkdir(parents=True, exist_ok=True) + manifest_paths = {} + + for split, rows in splits.items(): + out = output_path.parent / f"{output_path.stem}_{split}.csv" + with open(out, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=["filepath", "label", "generator"]) + writer.writeheader() + writer.writerows(rows) + manifest_paths[split] = out + + return manifest_paths + + +def validate_manifest(manifest_path: Path) -> dict: + """Check a manifest CSV is well-formed and all files exist.""" + missing = [] + counts = {"total": 0, "real": 0, "fake": 0} + + with open(manifest_path) as f: + for row in csv.DictReader(f): + counts["total"] += 1 + if int(row["label"]) == 0: + counts["real"] += 1 + else: + counts["fake"] += 1 + if not Path(row["filepath"]).exists(): + missing.append(row["filepath"]) + + return {"counts": counts, "missing": missing, "ok": len(missing) == 0} diff --git a/src/training/metrics.py b/src/training/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..f51fe9d7f8a4f1b3da78089eda680f2ea27f62f4 --- /dev/null +++ b/src/training/metrics.py @@ -0,0 +1,50 @@ +"""src/training/metrics.py — Shared metric computation utilities.""" +from __future__ import annotations + +import numpy as np +from sklearn.metrics import ( + roc_auc_score, f1_score, accuracy_score, + precision_score, recall_score, average_precision_score, + roc_curve, +) + + +def compute_auc(y_true: list, y_scores: list) -> float: + if len(set(y_true)) < 2: + return 0.5 + return float(roc_auc_score(y_true, y_scores)) + + +def compute_eer(y_true: list, y_scores: list) -> float: + if len(set(y_true)) < 2: + return 0.5 + fpr, tpr, _ = roc_curve(y_true, y_scores) + idx = int(np.argmin(np.abs(fpr - (1 - tpr)))) + return float(fpr[idx]) + + +def optimal_threshold(y_true: list, y_scores: list) -> float: + """Threshold that maximises F1 on the provided set.""" + from sklearn.metrics import precision_recall_curve + precision, recall, thresholds = precision_recall_curve(y_true, y_scores) + f1 = 2 * precision * recall / (precision + recall + 1e-8) + best = int(np.argmax(f1[:-1])) + return float(thresholds[best]) + + +def compute_all( + y_true: list, + y_scores: list, + threshold: float = 0.5, +) -> dict: + y_pred = (np.array(y_scores) >= threshold).astype(int) + return { + "auc": compute_auc(y_true, y_scores), + "auc_pr": float(average_precision_score(y_true, y_scores)) if len(set(y_true)) > 1 else 0.5, + "eer": compute_eer(y_true, y_scores), + "f1": float(f1_score(y_true, y_pred, zero_division=0)), + "accuracy": float(accuracy_score(y_true, y_pred)), + "precision": float(precision_score(y_true, y_pred, zero_division=0)), + "recall": float(recall_score(y_true, y_pred, zero_division=0)), + "threshold": threshold, + } diff --git a/src/types.py b/src/types.py new file mode 100644 index 0000000000000000000000000000000000000000..ebaeb3cf03dd578169ee39dd578fb856c80a1d3f --- /dev/null +++ b/src/types.py @@ -0,0 +1,84 @@ +""" +Shared API contracts for GenAI-DeepDetect. + +These types are used by API routes, fusion, explainability, and frontend clients. +""" +from __future__ import annotations + +from enum import Enum +from typing import Literal, Optional + +from pydantic import BaseModel, field_validator + + +class GeneratorLabel(str, Enum): + """Generator attribution labels used across the pipeline.""" + + real = "real" + unknown_gan = "unknown_gan" + stable_diffusion = "stable_diffusion" + midjourney = "midjourney" + dall_e = "dall_e" + flux = "flux" + firefly = "firefly" + imagen = "imagen" + + +GENERATOR_INDEX_TO_LABEL: dict[int, GeneratorLabel] = { + 0: GeneratorLabel.real, + 1: GeneratorLabel.unknown_gan, + 2: GeneratorLabel.stable_diffusion, + 3: GeneratorLabel.midjourney, + 4: GeneratorLabel.dall_e, + 5: GeneratorLabel.flux, + 6: GeneratorLabel.firefly, + 7: GeneratorLabel.imagen, +} + + +class EngineResult(BaseModel): + """Single-engine inference output.""" + + engine: str + verdict: Literal["FAKE", "REAL", "UNKNOWN"] + confidence: float + attributed_generator: Optional[str] = None + explanation: str = "" + processing_time_ms: float = 0.0 + + @field_validator("confidence") + @classmethod + def confidence_in_range(cls, value: float) -> float: + if not 0.0 <= value <= 1.0: + raise ValueError(f"confidence must be in [0,1], got {value}") + return round(float(value), 4) + + @field_validator("processing_time_ms") + @classmethod + def processing_time_non_negative(cls, value: float) -> float: + if value < 0: + raise ValueError("processing_time_ms must be >= 0") + return round(float(value), 2) + + +class DetectionResponse(BaseModel): + """Unified API response for image/video detection endpoints.""" + + verdict: Literal["FAKE", "REAL", "UNKNOWN"] + confidence: float + attributed_generator: str + explanation: str + processing_time_ms: float + engine_breakdown: list[EngineResult] + + # Optional explainability metadata + clarity_score: Optional[float] = None + saliency_map_url: Optional[str] = None + structured_explanation: Optional[dict] = None + + @field_validator("confidence") + @classmethod + def response_confidence_in_range(cls, value: float) -> float: + if not 0.0 <= value <= 1.0: + raise ValueError(f"confidence must be in [0,1], got {value}") + return round(float(value), 4) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..d0aa4ec96b8548af85d69979f3163b9654031f35 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,70 @@ +"""tests/conftest.py — shared pytest fixtures""" +from __future__ import annotations + +import io +import sys +from pathlib import Path + +import numpy as np +import pytest +from PIL import Image + +# Ensure repo root is on path +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +@pytest.fixture +def fake_image_bytes() -> bytes: + """Returns JPEG bytes of a random 224×224 RGB image.""" + arr = (np.random.rand(224, 224, 3) * 255).astype(np.uint8) + img = Image.fromarray(arr) + buf = io.BytesIO() + img.save(buf, format="JPEG") + return buf.getvalue() + + +@pytest.fixture +def fake_image_bytes_png() -> bytes: + arr = (np.random.rand(224, 224, 3) * 255).astype(np.uint8) + img = Image.fromarray(arr) + buf = io.BytesIO() + img.save(buf, format="PNG") + return buf.getvalue() + + +@pytest.fixture +def fake_landmark_sequence() -> "np.ndarray": + """Returns (64, 68, 3) float32 landmark array with random values in [0,1].""" + return np.random.rand(64, 68, 3).astype(np.float32) + + +@pytest.fixture +def fake_lip_mfcc(): + """Returns (lip_frames, mfcc) as numpy arrays.""" + lip = np.random.rand(25, 64, 96, 3).astype(np.float32) + mfcc = np.random.rand(40, 50).astype(np.float32) + return lip, mfcc + + +@pytest.fixture +def engine_result_fake(): + from src.types import EngineResult + return EngineResult( + engine="fingerprint", + verdict="FAKE", + confidence=0.92, + explanation="Test explanation.", + processing_time_ms=50.0, + ) + + +@pytest.fixture +def engine_result_real(): + from src.types import EngineResult + return EngineResult( + engine="fingerprint", + verdict="REAL", + confidence=0.85, + explanation="No synthetic fingerprints found.", + processing_time_ms=50.0, + ) diff --git a/tests/engines/__init__.py b/tests/engines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/engines/test_coherence.py b/tests/engines/test_coherence.py new file mode 100644 index 0000000000000000000000000000000000000000..a516477b5b5902d16bd55af04e3956fa01a07953 --- /dev/null +++ b/tests/engines/test_coherence.py @@ -0,0 +1,35 @@ +"""tests/engines/test_coherence.py""" +from __future__ import annotations + +import pytest +from src.types import EngineResult + + +def test_coherence_detector_imports(): + from src.engines.coherence.detector import CoherenceDetector + assert CoherenceDetector is not None + + +def test_coherence_image_stub(): + from src.engines.coherence.detector import CoherenceDetector + stub = CoherenceDetector.image_stub() + assert isinstance(stub, EngineResult) + assert stub.engine == "coherence" + assert stub.verdict in ("FAKE", "REAL") + assert "N/A" in stub.explanation + assert stub.processing_time_ms == 0.0 + + +def test_coherence_detector_init(): + from src.engines.coherence.detector import CoherenceDetector + det = CoherenceDetector() + assert 0.0 < det.threshold <= 1.0 + + +def test_coherence_error_result_is_valid(): + from src.engines.coherence.detector import CoherenceDetector + det = CoherenceDetector() + result = det._error_result(0.05) + assert isinstance(result, EngineResult) + assert result.verdict in ("FAKE", "REAL") + assert 0.0 <= result.confidence <= 1.0 diff --git a/tests/engines/test_fingerprint.py b/tests/engines/test_fingerprint.py new file mode 100644 index 0000000000000000000000000000000000000000..21d3127d5a50f0ba695435d7cdea81a93a25e8bf --- /dev/null +++ b/tests/engines/test_fingerprint.py @@ -0,0 +1,52 @@ +"""tests/engines/test_fingerprint.py""" +from __future__ import annotations + +import pytest +from src.types import EngineResult + + +def test_fingerprint_detector_imports(): + from src.engines.fingerprint.detector import FingerprintDetector + assert FingerprintDetector is not None + + +def test_fingerprint_detector_init(): + from src.engines.fingerprint.detector import FingerprintDetector + det = FingerprintDetector() + assert det.model is not None + assert 0.0 < det.threshold <= 1.0 + + +def test_fingerprint_detect_bytes_returns_engine_result(fake_image_bytes): + from src.engines.fingerprint.detector import FingerprintDetector + det = FingerprintDetector() + result = det.detect_bytes(fake_image_bytes) + assert isinstance(result, EngineResult) + assert result.engine == "fingerprint" + assert result.verdict in ("FAKE", "REAL") + assert 0.0 <= result.confidence <= 1.0 + assert result.processing_time_ms > 0 + assert len(result.explanation) > 0 + + +def test_fingerprint_result_confidence_range(fake_image_bytes): + from src.engines.fingerprint.detector import FingerprintDetector + det = FingerprintDetector() + result = det.detect_bytes(fake_image_bytes) + assert result.confidence >= 0.0 + assert result.confidence <= 1.0 + + +def test_fingerprint_image_stub(): + from src.engines.fingerprint.detector import FingerprintDetector + stub = FingerprintDetector.image_stub() + assert isinstance(stub, EngineResult) + + +def test_fingerprint_handles_corrupt_bytes(): + from src.engines.fingerprint.detector import FingerprintDetector + det = FingerprintDetector() + result = det.detect_bytes(b"not an image") + # Must not raise — should return error result + assert isinstance(result, EngineResult) + assert result.verdict in ("FAKE", "REAL") diff --git a/tests/engines/test_sstgnn.py b/tests/engines/test_sstgnn.py new file mode 100644 index 0000000000000000000000000000000000000000..b2101941b4b2ff0d549c0a6827f3a5a58a2f07c9 --- /dev/null +++ b/tests/engines/test_sstgnn.py @@ -0,0 +1,58 @@ +"""tests/engines/test_sstgnn.py""" +from __future__ import annotations + +import numpy as np +import pytest +from src.types import EngineResult + + +def test_sstgnn_detector_imports(): + from src.engines.sstgnn.detector import SSTGNNDetector + assert SSTGNNDetector is not None + + +def test_sstgnn_image_stub(): + from src.engines.sstgnn.detector import SSTGNNDetector + stub = SSTGNNDetector.image_stub() + assert isinstance(stub, EngineResult) + assert stub.engine == "sstgnn" + assert stub.verdict in ("FAKE", "REAL") + assert "N/A" in stub.explanation + + +def test_sstgnn_detector_init(): + from src.engines.sstgnn.detector import SSTGNNDetector + det = SSTGNNDetector() + assert 0.0 < det.threshold <= 1.0 + + +def test_sstgnn_heuristic_fallback(fake_landmark_sequence): + """Heuristic detect should work without a trained model.""" + from src.engines.sstgnn.detector import SSTGNNDetector + det = SSTGNNDetector() + t0 = 0.0 + result = det._detect_heuristic(fake_landmark_sequence, t0) + assert isinstance(result, EngineResult) + assert result.verdict in ("FAKE", "REAL") + assert 0.0 <= result.confidence <= 1.0 + + +def test_graph_builder_output_shape(fake_landmark_sequence): + """Graph builder must produce correct node/edge shapes.""" + pytest.importorskip("torch_geometric") + from src.engines.sstgnn.graph_builder import build_temporal_graph + + graph = build_temporal_graph(fake_landmark_sequence, label=1) + T, L, _ = fake_landmark_sequence.shape + assert graph.x.shape == (T * L, 5) + assert graph.edge_index.shape[0] == 2 + assert graph.edge_attr.shape[1] == 1 + assert graph.y is not None + + +def test_graph_builder_label_minus_one(fake_landmark_sequence): + """label=-1 should not set graph.y.""" + pytest.importorskip("torch_geometric") + from src.engines.sstgnn.graph_builder import build_temporal_graph + graph = build_temporal_graph(fake_landmark_sequence, label=-1) + assert not hasattr(graph, "y") or graph.y is None diff --git a/tests/services/test_inference_router.py b/tests/services/test_inference_router.py new file mode 100644 index 0000000000000000000000000000000000000000..d97fbc91c27df0667bd55205e7a4ca634068da31 --- /dev/null +++ b/tests/services/test_inference_router.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from src.services.inference_router import get_inference_backend, is_runpod_configured + + +def test_get_inference_backend_defaults_to_auto(monkeypatch): + monkeypatch.delenv("INFERENCE_BACKEND", raising=False) + assert get_inference_backend() == "auto" + + +def test_get_inference_backend_accepts_valid_values(monkeypatch): + monkeypatch.setenv("INFERENCE_BACKEND", "local") + assert get_inference_backend() == "local" + + monkeypatch.setenv("INFERENCE_BACKEND", "runpod") + assert get_inference_backend() == "runpod" + + monkeypatch.setenv("INFERENCE_BACKEND", "hf") + assert get_inference_backend() == "hf" + + monkeypatch.setenv("INFERENCE_BACKEND", "auto") + assert get_inference_backend() == "auto" + + +def test_get_inference_backend_invalid_value_falls_back_to_auto(monkeypatch): + monkeypatch.setenv("INFERENCE_BACKEND", "invalid_mode") + assert get_inference_backend() == "auto" + + +def test_is_runpod_configured_false_on_placeholder_endpoint(monkeypatch): + monkeypatch.setenv("RUNPOD_API_KEY", "rpa_test") + monkeypatch.setenv("RUNPOD_ENDPOINT_ID", "your_serverless_endpoint_id_here") + assert is_runpod_configured() is False + + +def test_is_runpod_configured_true_when_key_and_endpoint_set(monkeypatch): + monkeypatch.setenv("RUNPOD_API_KEY", "rpa_test") + monkeypatch.setenv("RUNPOD_ENDPOINT_ID", "abcd1234") + assert is_runpod_configured() is True diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..a68bed15298c7673f7cd7d0bb3a33f6791148f5c --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,136 @@ +"""tests/test_api.py — FastAPI endpoint integration tests.""" +from __future__ import annotations + +import io +import pytest +import numpy as np +from PIL import Image +from fastapi.testclient import TestClient + + +@pytest.fixture(scope="module") +def client(): + from src.api.main import app + return TestClient(app) + + +@pytest.fixture +def jpeg_bytes(): + arr = (np.random.rand(224, 224, 3) * 255).astype(np.uint8) + buf = io.BytesIO() + Image.fromarray(arr).save(buf, format="JPEG") + return buf.getvalue() + + +# ── GET /health ─────────────────────────────────────────────────────────────── + +def test_health_returns_200(client): + r = client.get("/health") + assert r.status_code == 200 + + +def test_health_has_required_fields(client): + data = client.get("/health").json() + assert data["status"] == "ok" + assert "version" in data + assert "engines" in data + assert "inference_backend" in data + assert "runpod_configured" in data + assert set(data["engines"]) == {"fingerprint", "coherence", "sstgnn"} + + +def test_health_models_returns_inventory(client): + data = client.get("/health/models").json() + assert "fingerprint" in data + assert "coherence" in data + assert "sstgnn" in data + assert "generator_labels" in data + assert "stable_diffusion" in data["generator_labels"] + + +# ── GET / ───────────────────────────────────────────────────────────────────── + +def test_root_returns_html(client): + r = client.get("/") + assert r.status_code == 200 + assert "text/html" in r.headers["content-type"] + + +# ── POST /detect/image ──────────────────────────────────────────────────────── + +def test_detect_image_returns_200(client, jpeg_bytes): + r = client.post( + "/detect/image", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ) + assert r.status_code == 200 + + +def test_detect_image_response_schema(client, jpeg_bytes): + data = client.post( + "/detect/image", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ).json() + + assert data["verdict"] in ("FAKE", "REAL") + assert 0.0 <= data["confidence"] <= 1.0 + assert "attributed_generator" in data + assert "explanation" in data + assert "engine_breakdown" in data + assert len(data["engine_breakdown"]) == 3 + + +def test_detect_image_engine_names(client, jpeg_bytes): + data = client.post( + "/detect/image", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ).json() + + engine_names = {e["engine"] for e in data["engine_breakdown"]} + assert engine_names == {"fingerprint", "coherence", "sstgnn"} + + +def test_detect_image_engine_confidence_range(client, jpeg_bytes): + data = client.post( + "/detect/image", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ).json() + + for engine in data["engine_breakdown"]: + assert 0.0 <= engine["confidence"] <= 1.0 + assert engine["verdict"] in ("FAKE", "REAL") + + +def test_detect_image_too_large_returns_413(client): + big = b"x" * (21 * 1024 * 1024) # 21MB > 20MB limit + r = client.post( + "/detect/image", + files={"file": ("big.jpg", big, "image/jpeg")}, + ) + assert r.status_code == 413 + + +def test_detect_image_wrong_type_returns_415(client, jpeg_bytes): + r = client.post( + "/detect/image", + files={"file": ("test.mp4", jpeg_bytes, "video/mp4")}, + ) + assert r.status_code == 415 + + +def test_detect_image_processing_time_positive(client, jpeg_bytes): + data = client.post( + "/detect/image", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ).json() + assert data["processing_time_ms"] >= 0 + + +# ── POST /detect/video ──────────────────────────────────────────────────────── + +def test_detect_video_wrong_type_returns_415(client, jpeg_bytes): + r = client.post( + "/detect/video", + files={"file": ("test.jpg", jpeg_bytes, "image/jpeg")}, + ) + assert r.status_code == 415 diff --git a/tests/test_fusion.py b/tests/test_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..fb2605855988b39cca102444a7a90eb40d662316 --- /dev/null +++ b/tests/test_fusion.py @@ -0,0 +1,100 @@ +"""tests/test_fusion.py""" +from __future__ import annotations + +import pytest +from src.types import EngineResult, GeneratorLabel + + +def _make_result(engine, verdict, conf, explanation="Test"): + return EngineResult( + engine=engine, verdict=verdict, confidence=conf, + explanation=explanation, processing_time_ms=100.0 + ) + + +def test_fuser_imports(): + from src.fusion.fuser import Fuser + assert Fuser is not None + + +def test_fuser_image_returns_detection_response(engine_result_fake): + from src.fusion.fuser import Fuser + from src.engines.coherence.detector import CoherenceDetector + from src.engines.sstgnn.detector import SSTGNNDetector + fuser = Fuser() + results = [engine_result_fake, CoherenceDetector.image_stub(), SSTGNNDetector.image_stub()] + response = fuser.fuse(results, media_type="image", total_ms=100.0) + from src.types import DetectionResponse + assert isinstance(response, DetectionResponse) + assert response.verdict in ("FAKE", "REAL") + assert 0.0 <= response.confidence <= 1.0 + assert len(response.engine_breakdown) == 3 + + +def test_fuser_high_fake_confidence_returns_fake(): + from src.fusion.fuser import Fuser + fuser = Fuser() + results = [ + _make_result("fingerprint", "FAKE", 0.95), + _make_result("coherence", "FAKE", 0.88), + _make_result("sstgnn", "FAKE", 0.82), + ] + resp = fuser.fuse(results, media_type="video", total_ms=500.0) + assert resp.verdict == "FAKE" + assert resp.confidence > 0.5 + + +def test_fuser_high_real_confidence_returns_real(): + from src.fusion.fuser import Fuser + fuser = Fuser() + results = [ + _make_result("fingerprint", "REAL", 0.93), + _make_result("coherence", "REAL", 0.88), + _make_result("sstgnn", "REAL", 0.85), + ] + resp = fuser.fuse(results, media_type="video", total_ms=500.0) + assert resp.verdict == "REAL" + + +def test_fuser_empty_returns_default(): + from src.fusion.fuser import Fuser + fuser = Fuser() + resp = fuser.fuse([], media_type="image", total_ms=0.0) + assert resp.verdict in ("FAKE", "REAL") + + +def test_fuser_real_verdict_sets_generator_real(): + from src.fusion.fuser import Fuser + fuser = Fuser() + results = [_make_result("fingerprint", "REAL", 0.95)] + resp = fuser.fuse(results, media_type="image", total_ms=100.0) + if resp.verdict == "REAL": + assert resp.attributed_generator == GeneratorLabel.real + + +def test_types_generator_label_count(): + assert len(GeneratorLabel) == 8 + + +def test_types_generator_index_mapping(): + from src.types import GENERATOR_INDEX_TO_LABEL + assert GENERATOR_INDEX_TO_LABEL[0] == GeneratorLabel.real + assert GENERATOR_INDEX_TO_LABEL[1] == GeneratorLabel.unknown_gan + assert GENERATOR_INDEX_TO_LABEL[2] == GeneratorLabel.stable_diffusion + assert GENERATOR_INDEX_TO_LABEL[7] == GeneratorLabel.imagen + + +def test_engine_result_confidence_validator(): + with pytest.raises(Exception): + EngineResult( + engine="test", verdict="FAKE", confidence=1.5, + explanation="x", processing_time_ms=1.0 + ) + + +def test_engine_result_confidence_rounds(): + r = EngineResult( + engine="test", verdict="FAKE", confidence=0.923456789, + explanation="x", processing_time_ms=1.0 + ) + assert len(str(r.confidence).split(".")[-1]) <= 4 diff --git a/tests/training/__init__.py b/tests/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/training/test_datasets.py b/tests/training/test_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..fafe323edeaaf17620a1fe70137dd12706931c99 --- /dev/null +++ b/tests/training/test_datasets.py @@ -0,0 +1,108 @@ +"""tests/training/test_datasets.py""" +from __future__ import annotations + +import shutil +import uuid +from pathlib import Path + +import numpy as np +from PIL import Image + + +def _make_tiny_fingerprint_dataset(root: Path, n_per_class: int = 10) -> None: + """Create a minimal fake fingerprint dataset for testing.""" + for split in ["train", "val", "test"]: + for label in ["real", "fake"]: + d = root / split / label + d.mkdir(parents=True) + for i in range(n_per_class): + arr = (np.random.rand(64, 64, 3) * 255).astype(np.uint8) + Image.fromarray(arr).save(d / f"img_{i:04d}.jpg") + + +def _new_workspace_tmp(prefix: str) -> Path: + root = Path("tests") / ".tmp" / f"{prefix}_{uuid.uuid4().hex}" + root.mkdir(parents=True, exist_ok=True) + return root + + +def test_training_config_num_generator_classes(): + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from src.training.config import NUM_GENERATOR_CLASSES, GENERATOR_CLASSES + assert NUM_GENERATOR_CLASSES == 8 + assert len(GENERATOR_CLASSES) == 8 + assert GENERATOR_CLASSES[0] == "real" + assert GENERATOR_CLASSES[7] == "imagen" + + +def test_training_config_dataclass_defaults(): + from src.training.config import TrainingConfig + cfg = TrainingConfig() + assert cfg.num_generator_classes == 8 + assert cfg.num_binary_classes == 2 + assert cfg.seed == 42 + assert cfg.amp is True + + +def test_build_dataset_runs(): + """Smoke test: build_dataset.py should run without crashing on tiny data.""" + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + + tmp_path = _new_workspace_tmp("build_dataset") + try: + # Create tiny fake source datasets + faces_dir = tmp_path / "faces_140k" + (faces_dir / "real").mkdir(parents=True) + (faces_dir / "fake").mkdir(parents=True) + for i in range(20): + for label in ["real", "fake"]: + arr = (np.random.rand(64, 64, 3) * 255).astype(np.uint8) + Image.fromarray(arr).save(faces_dir / label / f"img_{i}.jpg") + + output_dir = tmp_path / "processed" + + from training.phase1_fingerprint.build_dataset import build + import argparse + + args = argparse.Namespace( + faces_140k=str(faces_dir), + ai_vs_real=None, + deepfake_real=None, + deepfake_faces=None, + celebdf=None, + output_dir=str(output_dir), + seed=42, + ) + build(args) + + # Check output structure + assert (output_dir / "generator_labels.csv").exists() + for split in ["train", "val", "test"]: + for label in ["real", "fake"]: + assert (output_dir / split / label).exists() + finally: + shutil.rmtree(tmp_path, ignore_errors=True) + + +def test_fingerprint_dataset_class(): + """FingerprintDataset should load images correctly.""" + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + tmp_path = _new_workspace_tmp("fingerprint_dataset") + try: + _make_tiny_fingerprint_dataset(tmp_path) + + from training.phase1_fingerprint.train import FingerprintDataset, get_val_transform + + ds = FingerprintDataset(tmp_path, "train", get_val_transform()) + + assert len(ds) == 20 # 10 real + 10 fake + sample = ds[0] + assert "image" in sample + assert "label" in sample + assert sample["image"].shape == (3, 224, 224) + assert sample["label"] in (0, 1) + finally: + shutil.rmtree(tmp_path, ignore_errors=True) diff --git a/tests/training/test_metrics.py b/tests/training/test_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1419469c89a5a8f70f2941d426edf997915d6a --- /dev/null +++ b/tests/training/test_metrics.py @@ -0,0 +1,66 @@ +"""tests/training/test_metrics.py""" +from __future__ import annotations + +import numpy as np +import pytest + + +def test_compute_auc_perfect(): + from src.training.metrics import compute_auc + y_true = [0, 0, 1, 1] + y_scores = [0.1, 0.2, 0.8, 0.9] + assert compute_auc(y_true, y_scores) == 1.0 + + +def test_compute_auc_random(): + from src.training.metrics import compute_auc + y_true = [0, 1, 0, 1] + y_scores = [0.4, 0.6, 0.5, 0.5] + auc = compute_auc(y_true, y_scores) + assert 0.0 <= auc <= 1.0 + + +def test_compute_auc_single_class_returns_half(): + from src.training.metrics import compute_auc + # All same class — undefined AUC, should return 0.5 + assert compute_auc([1, 1, 1], [0.9, 0.8, 0.7]) == 0.5 + + +def test_compute_eer_range(): + from src.training.metrics import compute_eer + y_true = [0, 0, 1, 1] + y_scores = [0.1, 0.3, 0.7, 0.9] + eer = compute_eer(y_true, y_scores) + assert 0.0 <= eer <= 1.0 + + +def test_optimal_threshold_returns_float(): + from src.training.metrics import optimal_threshold + y_true = [0, 0, 1, 1, 1] + y_scores = [0.1, 0.2, 0.6, 0.8, 0.9] + t = optimal_threshold(y_true, y_scores) + assert 0.0 <= t <= 1.0 + + +def test_compute_all_keys(): + from src.training.metrics import compute_all + y_true = [0, 0, 1, 1] + y_scores = [0.1, 0.2, 0.8, 0.9] + metrics = compute_all(y_true, y_scores, threshold=0.5) + for key in ["auc", "auc_pr", "eer", "f1", "accuracy", "precision", "recall", "threshold"]: + assert key in metrics, f"Missing key: {key}" + + +def test_training_config_consistency(): + """Generator class count must be 8 and consistent across config + types.""" + from src.training.config import NUM_GENERATOR_CLASSES, GENERATOR_CLASSES + from src.types import GeneratorLabel, GENERATOR_INDEX_TO_LABEL + + assert NUM_GENERATOR_CLASSES == 8 + assert len(GENERATOR_CLASSES) == 8 + assert len(GeneratorLabel) == 8 + assert len(GENERATOR_INDEX_TO_LABEL) == 8 + + # All class names must map to a valid GeneratorLabel + for name in GENERATOR_CLASSES: + assert GeneratorLabel(name) is not None diff --git a/training/download_datasets.sh b/training/download_datasets.sh new file mode 100644 index 0000000000000000000000000000000000000000..483966cab1030a0c235652b5144c669199c92e0d --- /dev/null +++ b/training/download_datasets.sh @@ -0,0 +1,71 @@ +#!/bin/bash +# training/download_datasets.sh +# +# Downloads all training datasets via Kaggle CLI. +# +# Prerequisites: +# 1. kaggle.json in ~/.kaggle/ (chmod 600) +# 2. DFDC competition acceptance at: +# https://www.kaggle.com/competitions/deepfake-detection-challenge +# +# Run: bash training/download_datasets.sh + +set -e + +echo "=== Downloading GenAI-DeepDetect training datasets ===" + +mkdir -p data/raw/ff++ +mkdir -p data/raw/140k_faces +mkdir -p data/raw/dfdc +mkdir -p data/raw/celebdf +mkdir -p data/raw/deepfake_faces +mkdir -p data/raw/deepfake_real_images +mkdir -p data/raw/ai_vs_real + +# 1. FaceForensics++ (compressed version) +echo "[1/7] Downloading FaceForensics++ ..." +kaggle datasets download -d xhlulu/faceforensics-in-compressed-videos \ + -p data/raw/ff++ --unzip +echo "[1/7] FaceForensics++ done" + +# 2. 140k Real vs Fake Faces — primary dataset for Fingerprint Engine +echo "[2/7] Downloading 140k Real vs Fake Faces ..." +kaggle datasets download -d xhlulu/140k-real-and-fake-faces \ + -p data/raw/140k_faces --unzip +echo "[2/7] 140k Faces done" + +# 3. DFDC — REQUIRES competition acceptance first! +# Go to: https://www.kaggle.com/competitions/deepfake-detection-challenge +# Click "Join Competition" and accept the rules. Then this works: +echo "[3/7] Downloading DFDC (requires prior competition acceptance) ..." +kaggle competitions download -c deepfake-detection-challenge \ + -p data/raw/dfdc --unzip +echo "[3/7] DFDC done" + +# 4. Celeb-DF v2 +echo "[4/7] Downloading Celeb-DF v2 ..." +kaggle datasets download -d reubensinclair/celeb-df \ + -p data/raw/celebdf --unzip +echo "[4/7] Celeb-DF done" + +# 5. Deepfake Faces (extra negative samples) +echo "[5/7] Downloading Deepfake Faces ..." +kaggle datasets download -d dagnelies/deepfake-faces \ + -p data/raw/deepfake_faces --unzip +echo "[5/7] Deepfake Faces done" + +# 6. Deepfake and Real Images +echo "[6/7] Downloading Deepfake and Real Images ..." +kaggle datasets download -d manjilkarki/deepfake-and-real-images \ + -p data/raw/deepfake_real_images --unzip +echo "[6/7] Deepfake Real Images done" + +# 7. AI Generated vs Real +echo "[7/7] Downloading AI Generated vs Real ..." +kaggle datasets download -d philosopher0/ai-generated-vs-real-images-datasaet \ + -p data/raw/ai_vs_real --unzip +echo "[7/7] AI vs Real done" + +echo "" +echo "=== All datasets downloaded ===" +du -sh data/raw/*/ diff --git a/training/phase1_fingerprint/__init__.py b/training/phase1_fingerprint/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase1_fingerprint/augmentation.py b/training/phase1_fingerprint/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..d884eeeba81bf4c335c044c3d9ac7d47f7eefd35 --- /dev/null +++ b/training/phase1_fingerprint/augmentation.py @@ -0,0 +1,78 @@ +""" +training/phase1_fingerprint/augmentation.py + +Albumentations augmentation pipelines for the Fingerprint engine. +ImageCompression augmentation is critical — without it, models learn +JPEG artifacts instead of synthetic fingerprints. +""" +from __future__ import annotations + +import numpy as np + +try: + import albumentations as A + from albumentations.pytorch import ToTensorV2 + ALBUMENTATIONS_AVAILABLE = True +except ImportError: + ALBUMENTATIONS_AVAILABLE = False + + +MEAN = [0.485, 0.456, 0.406] +STD = [0.229, 0.224, 0.225] + + +def get_train_transform(size: int = 224): + """Strong augmentation for training — simulates social-media compression.""" + if not ALBUMENTATIONS_AVAILABLE: + from torchvision import transforms + return transforms.Compose([ + transforms.RandomResizedCrop(size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(0.2, 0.2, 0.2, 0.1), + transforms.ToTensor(), + transforms.Normalize(MEAN, STD), + ]) + + return A.Compose([ + A.RandomResizedCrop(size, size, scale=(0.8, 1.0)), + A.HorizontalFlip(p=0.5), + A.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, p=0.5), + A.GaussNoise(var_limit=(10.0, 50.0), p=0.3), + # Simulate JPEG re-compression — critical for real-world robustness + A.ImageCompression(quality_lower=60, quality_upper=95, p=0.4), + A.Normalize(mean=MEAN, std=STD), + ToTensorV2(), + ]) + + +def get_val_transform(size: int = 224): + """Minimal deterministic transform for validation and inference.""" + if not ALBUMENTATIONS_AVAILABLE: + from torchvision import transforms + return transforms.Compose([ + transforms.Resize(int(size * 256 / 224)), + transforms.CenterCrop(size), + transforms.ToTensor(), + transforms.Normalize(MEAN, STD), + ]) + + return A.Compose([ + A.Resize(int(size * 256 / 224), int(size * 256 / 224)), + A.CenterCrop(size, size), + A.Normalize(mean=MEAN, std=STD), + ToTensorV2(), + ]) + + +def apply_transform(transform, image: np.ndarray) -> "torch.Tensor": + """ + Apply either a torchvision or albumentations transform to a numpy image. + image: (H, W, 3) uint8 + """ + if ALBUMENTATIONS_AVAILABLE and hasattr(transform, 'transforms'): + result = transform(image=image) + return result["image"] + else: + from PIL import Image + pil_img = Image.fromarray(image) + return transform(pil_img) diff --git a/training/phase1_fingerprint/build_dataset.py b/training/phase1_fingerprint/build_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f22b0f4b13912bd3d33178dbd6f5250fbff6f07e --- /dev/null +++ b/training/phase1_fingerprint/build_dataset.py @@ -0,0 +1,309 @@ +""" +training/phase1_fingerprint/build_dataset.py + +Builds a unified image dataset for the Fingerprint engine from multiple sources. + +Output structure: + data/processed/fingerprint/ + train/real/ + train/fake/ + val/real/ + val/fake/ + test/real/ + test/fake/ + generator_labels.csv ← filepath, is_fake, generator (0-7 int) + +Run: + python training/phase1_fingerprint/build_dataset.py --help +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import logging +import random +import shutil +import sys +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +# ── Logging ────────────────────────────────────────────────────────────────── + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%H:%M:%S", +) +log = logging.getLogger(__name__) + + +# ── Generator label mapping ─────────────────────────────────────────────────── + +GENERATOR_MAP: dict[str, int] = { + "real": 0, + "unknown_gan": 1, + "stable_diffusion": 2, + "midjourney": 3, + "dall_e": 4, + "flux": 5, + "firefly": 6, + "imagen": 7, +} + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + +def md5(path: Path) -> str: + h = hashlib.md5() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(65536), b""): + h.update(chunk) + return h.hexdigest() + + +def is_image(path: Path) -> bool: + return path.suffix.lower() in IMAGE_EXTS + + +def collect_images(root: Path) -> list[Path]: + return [p for p in root.rglob("*") if p.is_file() and is_image(p)] + + +# ── Dataset loaders ─────────────────────────────────────────────────────────── + +def load_140k_faces(root: Path) -> list[tuple[Path, int, int]]: + """Returns [(path, is_fake, generator_idx)]""" + records = [] + for split_dir in ["train", "valid"]: + for label in ["real", "fake"]: + d = root / split_dir / label + if not d.exists(): + # also try without nested split + d = root / label + if not d.exists(): + continue + is_fake = 1 if label == "fake" else 0 + gen = GENERATOR_MAP["unknown_gan"] if is_fake else GENERATOR_MAP["real"] + for p in collect_images(d): + records.append((p, is_fake, gen)) + log.info(f"140k_faces: {len(records)} images") + return records + + +def load_ai_vs_real(root: Path) -> list[tuple[Path, int, int]]: + """Subfolder name = generator name (or 'real').""" + records = [] + for subfolder in sorted(root.iterdir()): + if not subfolder.is_dir(): + continue + name = subfolder.name.lower().replace("-", "_").replace(" ", "_") + if "real" in name: + gen_idx = GENERATOR_MAP["real"] + is_fake = 0 + else: + # Map folder names to known generators + gen_idx = GENERATOR_MAP.get(name, GENERATOR_MAP["unknown_gan"]) + is_fake = 1 + for p in collect_images(subfolder): + records.append((p, is_fake, gen_idx)) + log.info(f"ai_vs_real: {len(records)} images") + return records + + +def load_deepfake_real_images(root: Path) -> list[tuple[Path, int, int]]: + """Folder structure: real/ and fake/ at root or one level down.""" + records = [] + for label in ["real", "fake"]: + # try root/label and root/*/label + candidates = list(root.glob(f"{label}")) + list(root.glob(f"*/{label}")) + for d in candidates: + if not d.is_dir(): + continue + is_fake = 1 if label == "fake" else 0 + gen = GENERATOR_MAP["unknown_gan"] if is_fake else GENERATOR_MAP["real"] + for p in collect_images(d): + records.append((p, is_fake, gen)) + log.info(f"deepfake_real_images: {len(records)} images") + return records + + +def load_deepfake_faces(root: Path) -> list[tuple[Path, int, int]]: + """All fake, generator = unknown_gan.""" + records = [] + for p in collect_images(root): + records.append((p, 1, GENERATOR_MAP["unknown_gan"])) + log.info(f"deepfake_faces: {len(records)} images (all fake)") + return records + + +def load_celebdf_images(root: Path) -> list[tuple[Path, int, int]]: + """Treat all images in 'Celeb-real'/'YouTube-real' as real, rest as fake.""" + records = [] + for p in collect_images(root): + parts = [part.lower() for part in p.parts] + is_real = any("real" in part for part in parts) + is_fake = 0 if is_real else 1 + gen = GENERATOR_MAP["real"] if is_real else GENERATOR_MAP["unknown_gan"] + records.append((p, is_fake, gen)) + log.info(f"celebdf: {len(records)} images") + return records + + +# ── Splitting + copying ─────────────────────────────────────────────────────── + +def split_records( + records: list[tuple[Path, int, int]], + seed: int = 42, + train_ratio: float = 0.80, + val_ratio: float = 0.10, +) -> dict[str, list[tuple[Path, int, int]]]: + rng = random.Random(seed) + shuffled = list(records) + rng.shuffle(shuffled) + n = len(shuffled) + n_train = int(n * train_ratio) + n_val = int(n * val_ratio) + return { + "train": shuffled[:n_train], + "val": shuffled[n_train:n_train + n_val], + "test": shuffled[n_train + n_val:], + } + + +def copy_to_split( + records: list[tuple[Path, int, int]], + split: str, + output_dir: Path, + seen_hashes: set[str], +) -> list[dict]: + """Copy images to output_dir/{split}/{real|fake}/ and return CSV rows.""" + rows = [] + dupes = 0 + for src, is_fake, gen in records: + h = md5(src) + if h in seen_hashes: + dupes += 1 + continue + seen_hashes.add(h) + + label_dir = output_dir / split / ("fake" if is_fake else "real") + label_dir.mkdir(parents=True, exist_ok=True) + + # Unique filename: hash prefix + original name + dst = label_dir / f"{h[:8]}_{src.name}" + if not dst.exists(): + shutil.copy2(src, dst) + + rows.append({ + "filepath": str(dst.relative_to(output_dir)), + "is_fake": is_fake, + "generator": gen, + }) + + if dupes: + log.warning(f" {split}: skipped {dupes} duplicate files") + return rows + + +# ── Main ───────────────────────────────────────────────────────────────────── + +def build(args: argparse.Namespace) -> None: + rng_seed = args.seed + np.random.seed(rng_seed) + random.seed(rng_seed) + + output_dir = Path(args.output_dir) + + # ── Load all sources ────────────────────────────────────────────────────── + all_records: list[tuple[Path, int, int]] = [] + + if args.faces_140k and Path(args.faces_140k).exists(): + all_records += load_140k_faces(Path(args.faces_140k)) + if args.ai_vs_real and Path(args.ai_vs_real).exists(): + all_records += load_ai_vs_real(Path(args.ai_vs_real)) + if args.deepfake_real and Path(args.deepfake_real).exists(): + all_records += load_deepfake_real_images(Path(args.deepfake_real)) + if args.deepfake_faces and Path(args.deepfake_faces).exists(): + all_records += load_deepfake_faces(Path(args.deepfake_faces)) + if args.celebdf and Path(args.celebdf).exists(): + all_records += load_celebdf_images(Path(args.celebdf)) + + if not all_records: + log.error("No images loaded — check dataset paths with --help") + sys.exit(1) + + log.info(f"Total records loaded: {len(all_records)}") + + # ── Split ───────────────────────────────────────────────────────────────── + splits = split_records(all_records, seed=rng_seed) + for s, recs in splits.items(): + log.info(f" {s}: {len(recs)} images") + + # ── Assertions ──────────────────────────────────────────────────────────── + MIN_PER_SPLIT = int(getattr(args, "min_per_split", 1)) + for s, recs in splits.items(): + assert len(recs) >= MIN_PER_SPLIT, \ + f"Split '{s}' only has {len(recs)} images, need >= {MIN_PER_SPLIT}" + + # ── Copy + write CSV ────────────────────────────────────────────────────── + seen_hashes: set[str] = set() + all_csv_rows: list[dict] = [] + + for split, records in splits.items(): + log.info(f"Copying {split} split ...") + (output_dir / split / "real").mkdir(parents=True, exist_ok=True) + (output_dir / split / "fake").mkdir(parents=True, exist_ok=True) + rows = copy_to_split(records, split, output_dir, seen_hashes) + all_csv_rows += rows + log.info(f" {split}: {len(rows)} files written") + + csv_path = output_dir / "generator_labels.csv" + with open(csv_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=["filepath", "is_fake", "generator"]) + writer.writeheader() + writer.writerows(all_csv_rows) + + log.info(f"generator_labels.csv written: {len(all_csv_rows)} rows") + + # ── Summary ─────────────────────────────────────────────────────────────── + total = len(all_csv_rows) + n_fake = sum(r["is_fake"] for r in all_csv_rows) + n_real = total - n_fake + gen_dist = Counter(r["generator"] for r in all_csv_rows) + + print("\n=== Dataset summary ===") + print(f"Total images : {total:,}") + print(f"Real : {n_real:,} ({100*n_real/total:.1f}%)") + print(f"Fake : {n_fake:,} ({100*n_fake/total:.1f}%)") + print("\nGenerator distribution:") + gen_names = ["real", "unknown_gan", "stable_diffusion", "midjourney", + "dall_e", "flux", "firefly", "imagen"] + for idx, name in enumerate(gen_names): + count = gen_dist.get(idx, 0) + if count: + print(f" {idx} {name:20s}: {count:,}") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Build unified fingerprint dataset") + p.add_argument("--faces_140k", default=None, help="Path to 140k-real-and-fake-faces") + p.add_argument("--ai_vs_real", default=None, help="Path to ai-generated-vs-real-images") + p.add_argument("--deepfake_real", default=None, help="Path to deepfake-and-real-images") + p.add_argument("--deepfake_faces",default=None, help="Path to deepfake-faces") + p.add_argument("--celebdf", default=None, help="Path to celeb-df dataset") + p.add_argument("--output_dir", default="data/processed/fingerprint", + help="Where to write the processed dataset") + p.add_argument("--seed", type=int, default=42) + p.add_argument("--min_per_split", type=int, default=1000, + help="Minimum items required in each split") + return p.parse_args() + + +if __name__ == "__main__": + build(parse_args()) diff --git a/training/phase1_fingerprint/dataset.py b/training/phase1_fingerprint/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f13a5dc249a75d7b2b588a2e7000684c86b1a610 --- /dev/null +++ b/training/phase1_fingerprint/dataset.py @@ -0,0 +1,93 @@ +""" +training/phase1_fingerprint/dataset.py + +PyTorch Dataset for the Fingerprint engine. +Reads the directory structure produced by build_dataset.py. +""" +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Optional + +import torch +from torch.utils.data import Dataset +from PIL import Image + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + + +class FingerprintDataset(Dataset): + """ + Loads images from: + root_dir/{split}/real/*.jpg + root_dir/{split}/fake/*.jpg + + And generator labels from root_dir/generator_labels.csv. + """ + + def __init__( + self, + root_dir: Path, + split: str, # 'train' | 'val' | 'test' + transform = None, + ) -> None: + self.root_dir = Path(root_dir) + self.split = split + self.transform = transform + self.samples: list[dict] = [] + + # Load generator label CSV + gen_map: dict[str, int] = {} + csv_path = self.root_dir / "generator_labels.csv" + if csv_path.exists(): + with open(csv_path) as f: + for row in csv.DictReader(f): + gen_map[row["filepath"]] = int(row["generator"]) + + # Walk split dirs + split_dir = self.root_dir / split + for label_str, is_fake in [("real", 0), ("fake", 1)]: + d = split_dir / label_str + if not d.exists(): + continue + for p in sorted(d.iterdir()): + if p.suffix.lower() not in IMAGE_EXTS: + continue + rel = str(p.relative_to(self.root_dir)) + gen_idx = gen_map.get(rel, 0 if is_fake == 0 else 1) + self.samples.append({ + "path": p, + "label": is_fake, + "generator": gen_idx, + }) + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int) -> dict: + s = self.samples[idx] + img = Image.open(s["path"]).convert("RGB") + if self.transform: + import numpy as np + from training.phase1_fingerprint.augmentation import apply_transform + img_np = apply_transform(self.transform, img if isinstance(img, type(img)) else img) + if not isinstance(img_np, torch.Tensor): + from torchvision import transforms as T + img_np = T.ToTensor()(img) + else: + import torchvision.transforms.functional as TF + img_np = TF.to_tensor(img) + + return { + "image": img_np, + "label": s["label"], + "generator": s["generator"], + "filepath": str(s["path"]), + } + + def get_class_weights(self) -> torch.Tensor: + labels = [s["label"] for s in self.samples] + n_real = max(labels.count(0), 1) + n_fake = max(labels.count(1), 1) + return torch.tensor([1.0 / n_real, 1.0 / n_fake], dtype=torch.float32) diff --git a/training/phase1_fingerprint/integrate.py b/training/phase1_fingerprint/integrate.py new file mode 100644 index 0000000000000000000000000000000000000000..41b03158a190560035aafd56b962f3262b03b8cc --- /dev/null +++ b/training/phase1_fingerprint/integrate.py @@ -0,0 +1,95 @@ +""" +training/phase1_fingerprint/integrate.py + +Run after Kaggle training completes. +Validates the checkpoint, copies it to production, and prints integration steps. + +Usage: + python training/phase1_fingerprint/integrate.py \ + --ckpt models/checkpoints/fingerprint/best.pt +""" +from __future__ import annotations + +import argparse +import json +import shutil +from datetime import date +from pathlib import Path + +import torch + + +def main(args: argparse.Namespace) -> None: + ckpt_path = Path(args.ckpt) + assert ckpt_path.exists(), f"Checkpoint not found: {ckpt_path}" + + # Load and inspect + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + val_auc = ckpt.get("val_auc", "unknown") + epoch = ckpt.get("epoch", "unknown") + config = ckpt.get("config", {}) + + print(f"\n=== Fingerprint checkpoint ===") + print(f" Epoch: {epoch}") + print(f" Val AUC: {val_auc}") + print(f" Config: {json.dumps(config, indent=4)}") + + # Validate model loads correctly + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase1_fingerprint.model import build_fingerprint_model + + model = build_fingerprint_model(pretrained=False) + model.load_state_dict(ckpt["model_state"]) + model.eval() + print(f"\n Model loaded successfully.") + + # Quick forward pass + import torch + dummy = torch.randn(1, 3, 224, 224) + with torch.no_grad(): + out = model(dummy) + assert out["binary_logits"].shape == (1, 2) + assert out["generator_logits"].shape == (1, 8) + print(f" Forward pass OK — binary (1,2), generator (1,8).") + + # Copy to production + prod_dir = Path("models/checkpoints/fingerprint") + prod_dir.mkdir(parents=True, exist_ok=True) + prod_path = prod_dir / "production.pt" + shutil.copy2(ckpt_path, prod_path) + print(f"\n Copied to: {prod_path}") + + # Write README + readme_path = prod_dir / "README.txt" + with open(readme_path, "w") as f: + f.write(f"GenAI-DeepDetect Fingerprint Engine Checkpoint\n") + f.write(f"Training date: {date.today()}\n") + f.write(f"Val AUC: {val_auc}\n") + f.write(f"Epoch: {epoch}\n") + f.write(f"Config: {json.dumps(config, indent=2)}\n") + print(f" README written: {readme_path}") + + print(f""" +=== Integration steps === + +1. The detector already loads from this path automatically: + models/checkpoints/fingerprint/best.pt + models/checkpoints/fingerprint/production.pt + +2. Verify the server picks it up: + python -c "from src.engines.fingerprint.detector import FingerprintDetector; d = FingerprintDetector(); print('Loaded')" + +3. Run integration tests: + python training/phase6_export/integration_test.py +""") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--ckpt", default="models/checkpoints/fingerprint/best.pt") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase1_fingerprint/model.py b/training/phase1_fingerprint/model.py new file mode 100644 index 0000000000000000000000000000000000000000..98f622e305844e04f2c214bd6b174e20e5928734 --- /dev/null +++ b/training/phase1_fingerprint/model.py @@ -0,0 +1,134 @@ +""" +training/phase1_fingerprint/model.py + +ViT-B/16 fine-tuned for deepfake detection with two heads: + 1. binary_head: real vs fake (2 classes) + 2. generator_head: generator attribution (8 classes) + +Architecture basis: Causal Fingerprints (arXiv:2509.15406) +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import timm + +NUM_GENERATOR_CLASSES = 8 + + +class FingerprintModel(nn.Module): + """ + ViT-B/16 backbone with dual classification heads. + + forward(x) returns: + { + 'binary_logits': Tensor (B, 2) + 'generator_logits': Tensor (B, 8) + } + """ + + def __init__( + self, + model_name: str = "vit_base_patch16_224", + pretrained: bool = True, + num_binary_classes: int = 2, + num_generator_classes: int = NUM_GENERATOR_CLASSES, + dropout: float = 0.1, + ) -> None: + super().__init__() + + # Load backbone — drop default head + self.backbone = timm.create_model( + model_name, + pretrained=pretrained, + num_classes=0, # removes the head + global_pool="token", # returns [CLS] token, shape (B, 768) + ) + embed_dim = self.backbone.num_features # 768 for ViT-B + + self.drop = nn.Dropout(dropout) + + # Parallel heads on [CLS] embedding + self.binary_head = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(embed_dim // 2, num_binary_classes), + ) + self.generator_head = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(embed_dim // 2, num_generator_classes), + ) + + def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]: + features = self.backbone(x) # (B, 768) + features = self.drop(features) + return { + "binary_logits": self.binary_head(features), # (B, 2) + "generator_logits": self.generator_head(features), # (B, 8) + } + + @property + def config(self) -> dict: + return { + "num_binary_classes": 2, + "num_generator_classes": NUM_GENERATOR_CLASSES, + } + + +class FingerprintLoss(nn.Module): + """ + Combined binary + generator attribution loss. + + binary_weight: weight for real/fake classification (primary) + generator_weight: weight for generator attribution (secondary, default 0.3) + """ + + def __init__( + self, + class_weights: torch.Tensor | None = None, + generator_weight: float = 0.3, + ) -> None: + super().__init__() + self.generator_weight = generator_weight + + self.binary_loss = nn.CrossEntropyLoss( + weight=class_weights, + label_smoothing=0.05, + ) + # ignore_index=-1 for samples where generator is unknown + self.generator_loss = nn.CrossEntropyLoss( + ignore_index=-1, + label_smoothing=0.05, + ) + + def forward( + self, + outputs: dict[str, torch.Tensor], + binary_labels: torch.Tensor, + generator_labels: torch.Tensor, + ) -> dict[str, torch.Tensor]: + b_loss = self.binary_loss(outputs["binary_logits"], binary_labels) + g_loss = self.generator_loss(outputs["generator_logits"], generator_labels) + + total = b_loss + self.generator_weight * g_loss + + return { + "total": total, + "binary": b_loss, + "generator": g_loss, + } + + +def build_fingerprint_model( + pretrained: bool = True, + num_generator_classes: int = NUM_GENERATOR_CLASSES, +) -> FingerprintModel: + """Convenience constructor used by both training and detector.""" + return FingerprintModel( + pretrained=pretrained, + num_generator_classes=num_generator_classes, + ) diff --git a/training/phase1_fingerprint/train.py b/training/phase1_fingerprint/train.py new file mode 100644 index 0000000000000000000000000000000000000000..8d240049cd9fbb9ac726c7438e347b242829f2f4 --- /dev/null +++ b/training/phase1_fingerprint/train.py @@ -0,0 +1,397 @@ +""" +training/phase1_fingerprint/train.py + +Full training script for the Fingerprint engine. + +Kaggle usage: + !python scripts/train_image_baseline.py --data_dir ... --output_dir ... + +Direct usage: + python training/phase1_fingerprint/train.py \ + --data_dir data/processed/fingerprint \ + --output_dir models/checkpoints/fingerprint \ + --epochs 30 --batch_size 64 --lr 2e-5 --amp +""" + +from __future__ import annotations + +import argparse +import csv +import logging +import math +import random +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import GradScaler, autocast +from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler +from torchvision import transforms +from PIL import Image +from sklearn.metrics import ( + accuracy_score, roc_auc_score, f1_score, + precision_score, recall_score, confusion_matrix, + classification_report, +) +from tqdm import tqdm + +# Local imports (run from repo root) +import sys +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +# ── Logging ─────────────────────────────────────────────────────────────────── + +def setup_logging(output_dir: Path) -> None: + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = Path("training/logs") + log_dir.mkdir(parents=True, exist_ok=True) + log_file = log_dir / f"phase1_fingerprint_{ts}.log" + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + handlers=[ + logging.StreamHandler(), + logging.FileHandler(log_file), + ], + ) + +log = logging.getLogger(__name__) + + +# ── Transforms ──────────────────────────────────────────────────────────────── + +MEAN = [0.485, 0.456, 0.406] +STD = [0.229, 0.224, 0.225] + +def get_train_transform(): + return transforms.Compose([ + transforms.RandomResizedCrop(224, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(p=0.5), + transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), + transforms.ToTensor(), + # Simulate JPEG compression artifacts + transforms.RandomApply([ + transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 1.0)) + ], p=0.3), + transforms.Normalize(MEAN, STD), + ]) + +def get_val_transform(): + return transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(MEAN, STD), + ]) + + +# ── Dataset ─────────────────────────────────────────────────────────────────── + +class FingerprintDataset(Dataset): + def __init__(self, root_dir: Path, split: str, transform=None): + self.root_dir = Path(root_dir) + self.split = split + self.transform = transform + self.samples: list[dict] = [] + + # Load generator labels CSV + csv_path = self.root_dir / "generator_labels.csv" + gen_map: dict[str, dict] = {} + if csv_path.exists(): + with open(csv_path) as f: + for row in csv.DictReader(f): + gen_map[row["filepath"]] = row + + # Walk split directories + split_dir = self.root_dir / split + for label_dir in ["real", "fake"]: + d = split_dir / label_dir + if not d.exists(): + continue + is_fake = 1 if label_dir == "fake" else 0 + for img_path in sorted(d.iterdir()): + if img_path.suffix.lower() not in {".jpg", ".jpeg", ".png", ".webp"}: + continue + rel = str(img_path.relative_to(self.root_dir)) + gen_idx = int(gen_map[rel]["generator"]) if rel in gen_map else (1 if is_fake else 0) + self.samples.append({ + "path": img_path, + "label": is_fake, + "generator": gen_idx, + }) + + log.info(f"FingerprintDataset [{split}]: {len(self.samples)} samples") + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int) -> dict: + s = self.samples[idx] + img = Image.open(s["path"]).convert("RGB") + if self.transform: + img = self.transform(img) + return { + "image": img, + "label": s["label"], + "generator": s["generator"], + "filepath": str(s["path"]), + } + + def get_class_weights(self) -> torch.Tensor: + labels = [s["label"] for s in self.samples] + counts = [labels.count(0), labels.count(1)] + weights = [1.0 / (c + 1e-6) for c in counts] + return torch.tensor(weights, dtype=torch.float32) + + +# ── LR schedule ─────────────────────────────────────────────────────────────── + +def get_lr_with_warmup(step: int, warmup_steps: int, total_steps: int, base_lr: float) -> float: + if step < warmup_steps: + return base_lr * step / warmup_steps + progress = (step - warmup_steps) / max(1, total_steps - warmup_steps) + return base_lr * 0.5 * (1.0 + math.cos(math.pi * progress)) + + +# ── Training ────────────────────────────────────────────────────────────────── + +def train_epoch( + model, loader, criterion, optimizer, scaler, device, args, epoch, total_steps +) -> dict: + model.train() + total_loss = binary_loss_sum = gen_loss_sum = 0.0 + all_preds, all_labels = [], [] + step_offset = epoch * len(loader) + + pbar = tqdm(loader, desc=f"Train E{epoch+1}", leave=False) + for i, batch in enumerate(pbar): + images = batch["image"].to(device) + labels = batch["label"].to(device) + gen_lbls = batch["generator"].to(device) + + global_step = step_offset + i + + # LR warmup + lr = get_lr_with_warmup( + global_step, args.warmup_steps, + args.epochs * len(loader), args.lr + ) + for g in optimizer.param_groups: + g["lr"] = lr + + with autocast(enabled=args.amp): + outputs = model(images) + losses = criterion(outputs, labels, gen_lbls) + + loss = losses["total"] / args.grad_accum + scaler.scale(loss).backward() + + if (i + 1) % args.grad_accum == 0: + scaler.unscale_(optimizer) + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + total_loss += losses["total"].item() + binary_loss_sum += losses["binary"].item() + gen_loss_sum += losses["generator"].item() + + preds = outputs["binary_logits"].argmax(dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + if i % 50 == 0: + acc = accuracy_score(all_labels, all_preds) + pbar.set_postfix(loss=f"{losses['total'].item():.4f}", acc=f"{acc:.3f}", lr=f"{lr:.2e}") + + n = len(loader) + return { + "loss": total_loss / n, + "binary_loss": binary_loss_sum / n, + "gen_loss": gen_loss_sum / n, + "accuracy": accuracy_score(all_labels, all_preds), + } + + +@torch.no_grad() +def validate(model, loader, criterion, device, args) -> dict: + model.eval() + total_loss = 0.0 + all_preds, all_labels, all_probs = [], [], [] + + for batch in tqdm(loader, desc="Val", leave=False): + images = batch["image"].to(device) + labels = batch["label"].to(device) + gen_lbls = batch["generator"].to(device) + + with autocast(enabled=args.amp): + outputs = model(images) + losses = criterion(outputs, labels, gen_lbls) + + total_loss += losses["total"].item() + + probs = torch.softmax(outputs["binary_logits"], dim=1)[:, 1] + preds = outputs["binary_logits"].argmax(dim=1) + + all_probs.extend(probs.cpu().numpy()) + all_preds.extend(preds.cpu().numpy()) + all_labels.extend(labels.cpu().numpy()) + + auc = roc_auc_score(all_labels, all_probs) if len(set(all_labels)) > 1 else 0.5 + return { + "loss": total_loss / len(loader), + "auc": auc, + "accuracy": accuracy_score(all_labels, all_preds), + "f1": f1_score(all_labels, all_preds, zero_division=0), + "precision": precision_score(all_labels, all_preds, zero_division=0), + "recall": recall_score(all_labels, all_preds, zero_division=0), + } + + +def save_checkpoint(path: Path, model, optimizer, scaler, epoch, val_auc, args) -> None: + torch.save({ + "epoch": epoch, + "model_state": model.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "val_auc": val_auc, + "config": vars(args), + }, path) + + +def main(args: argparse.Namespace) -> None: + from training.phase1_fingerprint.model import FingerprintLoss, build_fingerprint_model + + setup_logging(Path(args.output_dir)) + torch.manual_seed(args.seed) + np.random.seed(args.seed) + random.seed(args.seed) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + log.info(f"Device: {device}") + if device.type == "cuda": + log.info(f"GPU: {torch.cuda.get_device_name(0)}") + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # ── Datasets ────────────────────────────────────────────────────────────── + train_ds = FingerprintDataset(args.data_dir, "train", get_train_transform()) + val_ds = FingerprintDataset(args.data_dir, "val", get_val_transform()) + + # Weighted sampler for class balance + weights = [train_ds.get_class_weights()[s["label"]] for s in train_ds.samples] + sampler = WeightedRandomSampler(weights, num_samples=len(weights), replacement=True) + + train_loader = DataLoader( + train_ds, batch_size=args.batch_size, sampler=sampler, + num_workers=args.workers, pin_memory=True, + ) + val_loader = DataLoader( + val_ds, batch_size=args.batch_size * 2, shuffle=False, + num_workers=args.workers, pin_memory=True, + ) + + # ── Model ───────────────────────────────────────────────────────────────── + model = build_fingerprint_model(pretrained=True).to(device) + class_weights = train_ds.get_class_weights().to(device) + criterion = FingerprintLoss(class_weights=class_weights) + + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.lr, weight_decay=args.weight_decay + ) + scaler = GradScaler(enabled=args.amp) + + # ── Resume ──────────────────────────────────────────────────────────────── + start_epoch = 0 + best_auc = 0.0 + no_improve = 0 + + if args.resume and Path(args.resume).exists(): + ckpt = torch.load(args.resume, map_location=device, weights_only=True) + model.load_state_dict(ckpt["model_state"]) + optimizer.load_state_dict(ckpt["optimizer"]) + scaler.load_state_dict(ckpt["scaler"]) + start_epoch = ckpt["epoch"] + 1 + best_auc = ckpt["val_auc"] + log.info(f"Resumed from {args.resume} at epoch {start_epoch}, best AUC={best_auc:.4f}") + + # ── Training loop ───────────────────────────────────────────────────────── + log.info(f"Training for {args.epochs} epochs") + for epoch in range(start_epoch, args.epochs): + t0 = time.time() + + train_metrics = train_epoch( + model, train_loader, criterion, optimizer, scaler, device, args, + epoch, args.epochs * len(train_loader) + ) + val_metrics = validate(model, val_loader, criterion, device, args) + + elapsed = time.time() - t0 + log.info( + f"Epoch {epoch+1:02d}/{args.epochs} | " + f"train_loss={train_metrics['loss']:.4f} | " + f"val_auc={val_metrics['auc']:.4f} | " + f"val_acc={val_metrics['accuracy']:.4f} | " + f"val_f1={val_metrics['f1']:.4f} | " + f"{elapsed:.0f}s" + ) + + # Save epoch checkpoint + save_checkpoint( + output_dir / f"epoch_{epoch+1:02d}.pt", + model, optimizer, scaler, epoch, val_metrics["auc"], args + ) + # Also save latest for --resume + save_checkpoint( + output_dir / "latest.pt", + model, optimizer, scaler, epoch, val_metrics["auc"], args + ) + + # Best checkpoint + if val_metrics["auc"] > best_auc: + best_auc = val_metrics["auc"] + no_improve = 0 + save_checkpoint( + output_dir / "best.pt", + model, optimizer, scaler, epoch, best_auc, args + ) + log.info(f" New best AUC: {best_auc:.4f} — checkpoint saved") + else: + no_improve += 1 + log.info(f" No improvement ({no_improve}/{args.patience})") + + if no_improve >= args.patience: + log.info(f"Early stopping triggered at epoch {epoch+1}") + break + + log.info(f"Training complete. Best val AUC: {best_auc:.4f}") + log.info(f"Best checkpoint: {output_dir / 'best.pt'}") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Train Fingerprint engine (Phase 1)") + p.add_argument("--data_dir", required=True, help="Path to processed fingerprint dataset") + p.add_argument("--output_dir", required=True, help="Where to save checkpoints") + p.add_argument("--epochs", type=int, default=30) + p.add_argument("--batch_size", type=int, default=64) + p.add_argument("--lr", type=float, default=2e-5) + p.add_argument("--weight_decay",type=float, default=0.01) + p.add_argument("--warmup_steps",type=int, default=500) + p.add_argument("--grad_accum", type=int, default=1) + p.add_argument("--workers", type=int, default=4) + p.add_argument("--patience", type=int, default=5) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--amp", action="store_true", default=False) + p.add_argument("--wandb", action="store_true", default=False) + p.add_argument("--resume", default=None, help="Resume from checkpoint path") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase2_coherence/__init__.py b/training/phase2_coherence/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase2_coherence/integrate.py b/training/phase2_coherence/integrate.py new file mode 100644 index 0000000000000000000000000000000000000000..5684fd8c690307cc90c83c7f05b5186b1ed1cf52 --- /dev/null +++ b/training/phase2_coherence/integrate.py @@ -0,0 +1,86 @@ +""" +training/phase2_coherence/integrate.py + +Post-training integration for the Coherence engine. +Validates checkpoint, copies to production path, prints integration steps. + +Usage: + python training/phase2_coherence/integrate.py \ + --ckpt models/checkpoints/coherence/best.pt +""" +from __future__ import annotations + +import argparse +import json +import shutil +from datetime import date +from pathlib import Path + +import torch + + +def main(args: argparse.Namespace) -> None: + ckpt_path = Path(args.ckpt) + assert ckpt_path.exists(), f"Checkpoint not found: {ckpt_path}" + + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + val_auc = ckpt.get("val_auc", "unknown") + epoch = ckpt.get("epoch", "unknown") + + print(f"\n=== Coherence checkpoint ===") + print(f" Epoch: {epoch}") + print(f" Val AUC: {val_auc}") + + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase2_coherence.model import CoherenceModel + + model = CoherenceModel() + model.load_state_dict(ckpt["model_state"]) + model.eval() + print(" Model loaded OK.") + + # Verify forward pass + dummy_lips = torch.randn(1, 25, 3, 64, 96) + dummy_mfcc = torch.randn(1, 40, 50) + with torch.no_grad(): + out = model(dummy_lips, dummy_mfcc) + assert "sync_score" in out and out["sync_score"].shape == (1,) + print(" Forward pass OK.") + + prod_dir = Path("models/checkpoints/coherence") + prod_dir.mkdir(parents=True, exist_ok=True) + prod_path = prod_dir / "production.pt" + shutil.copy2(ckpt_path, prod_path) + + readme = prod_dir / "README.txt" + with open(readme, "w") as f: + f.write(f"GenAI-DeepDetect Coherence Engine Checkpoint\n") + f.write(f"Training date: {date.today()}\n") + f.write(f"Val AUC: {val_auc}\n") + f.write(f"Epoch: {epoch}\n") + + print(f"\n Copied to: {prod_path}") + print(f""" +=== Integration steps === + +1. The detector auto-loads from: + models/checkpoints/coherence/best.pt + models/checkpoints/coherence/production.pt + +2. Test: + python -c "from src.engines.coherence.detector import CoherenceDetector; d = CoherenceDetector(); print('Loaded')" + +3. Run integration tests: + python training/phase6_export/integration_test.py +""") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--ckpt", default="models/checkpoints/coherence/best.pt") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase2_coherence/model.py b/training/phase2_coherence/model.py new file mode 100644 index 0000000000000000000000000000000000000000..3d8f11c66edf89ff6ab5e0d441b0062b942f759b --- /dev/null +++ b/training/phase2_coherence/model.py @@ -0,0 +1,169 @@ +""" +training/phase2_coherence/model.py + +Dual-stream lip-audio coherence model for video deepfake detection. + +Architecture basis: LipFD (NeurIPS 2024) + - Visual stream: TimeDistributed ResNet-18 → Temporal Transformer + - Audio stream: 1D-CNN over MFCC features + - Fusion: Concatenate → MLP → sync_score in [0, 1] + +Output: sync_score — 1.0 = synced (real), 0.0 = unsynced (fake) +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torchvision.models as tvm + + +class VisualStream(nn.Module): + """ + Processes a sequence of lip ROI frames. + Input: (B, T, C, H, W) — B batch, T=25 frames, C=3, H=64, W=96 + Output: (B, 256) — temporal summary embedding + """ + + def __init__(self, out_dim: int = 256) -> None: + super().__init__() + # ResNet-18 truncated at layer3 (output channels = 256) + rn = tvm.resnet18(weights=None) + self.frame_encoder = nn.Sequential( + rn.conv1, rn.bn1, rn.relu, rn.maxpool, + rn.layer1, rn.layer2, rn.layer3, # (B, 256, h, w) + ) + self.pool = nn.AdaptiveAvgPool2d(1) # → (B, 256, 1, 1) + + # Temporal Transformer — attends over 25 frame embeddings + encoder_layer = nn.TransformerEncoderLayer( + d_model=256, nhead=4, dim_feedforward=512, + dropout=0.1, batch_first=True, + ) + self.temporal_transformer = nn.TransformerEncoder(encoder_layer, num_layers=2) + self.cls_token = nn.Parameter(torch.zeros(1, 1, 256)) + nn.init.normal_(self.cls_token, std=0.02) + + self.proj = nn.Linear(256, out_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, T, C, H, W = x.shape + # Encode each frame independently + frames = x.view(B * T, C, H, W) + feat = self.frame_encoder(frames) # (B*T, 256, h, w) + feat = self.pool(feat).squeeze(-1).squeeze(-1) # (B*T, 256) + feat = feat.view(B, T, 256) # (B, T, 256) + + # Prepend [CLS] token + cls = self.cls_token.expand(B, -1, -1) + feat = torch.cat([cls, feat], dim=1) # (B, T+1, 256) + + out = self.temporal_transformer(feat) # (B, T+1, 256) + summary = out[:, 0, :] # [CLS] token → (B, 256) + return self.proj(summary) # (B, out_dim) + + +class AudioStream(nn.Module): + """ + Processes MFCC features. + Input: (B, 40, T) — 40 MFCC coefficients, T time frames + Output: (B, 256) + """ + + def __init__(self, out_dim: int = 256) -> None: + super().__init__() + self.net = nn.Sequential( + nn.Conv1d(40, 128, kernel_size=3, padding=1), + nn.BatchNorm1d(128), + nn.ReLU(inplace=True), + nn.Conv1d(128, 256, kernel_size=3, padding=1), + nn.BatchNorm1d(256), + nn.ReLU(inplace=True), + nn.Conv1d(256, 256, kernel_size=3, padding=1), + nn.BatchNorm1d(256), + nn.ReLU(inplace=True), + nn.AdaptiveAvgPool1d(1), # global average pool → (B, 256, 1) + ) + self.proj = nn.Linear(256, out_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + feat = self.net(x).squeeze(-1) # (B, 256) + return self.proj(feat) # (B, out_dim) + + +class CoherenceModel(nn.Module): + """ + Full dual-stream coherence model. + + forward(lip_frames, mfcc) returns: + { + 'sync_score': Tensor (B,) — 1=synced, 0=unsynced + 'visual_embed': Tensor (B, 256) + 'audio_embed': Tensor (B, 256) + } + + Use sync_logit (before sigmoid) with BCEWithLogitsLoss during training. + At inference: sync_score = sigmoid(sync_logit) + """ + + def __init__(self, embed_dim: int = 256, dropout: float = 0.3) -> None: + super().__init__() + self.visual_stream = VisualStream(out_dim=embed_dim) + self.audio_stream = AudioStream(out_dim=embed_dim) + + self.classifier = nn.Sequential( + nn.Linear(embed_dim * 2, 256), + nn.ReLU(inplace=True), + nn.Dropout(dropout), + nn.Linear(256, 1), # logit — apply sigmoid externally + ) + + def forward( + self, + lip_frames: torch.Tensor, # (B, T, C, H, W) + mfcc: torch.Tensor, # (B, 40, T_audio) + ) -> dict[str, torch.Tensor]: + v_embed = self.visual_stream(lip_frames) # (B, 256) + a_embed = self.audio_stream(mfcc) # (B, 256) + + fused = torch.cat([v_embed, a_embed], dim=1) # (B, 512) + logit = self.classifier(fused).squeeze(-1) # (B,) + + return { + "sync_score": torch.sigmoid(logit), + "sync_logit": logit, + "visual_embed": v_embed, + "audio_embed": a_embed, + } + + +class CoherenceLoss(nn.Module): + """BCE + optional contrastive loss between visual/audio embeddings.""" + + def __init__(self, contrastive_weight: float = 0.1) -> None: + super().__init__() + self.contrastive_weight = contrastive_weight + self.bce = nn.BCEWithLogitsLoss() + + def forward( + self, + outputs: dict[str, torch.Tensor], + labels: torch.Tensor, # 1=synced(real), 0=unsynced(fake) + ) -> dict[str, torch.Tensor]: + bce_loss = self.bce(outputs["sync_logit"], labels.float()) + + # Cosine contrastive: pull real pairs together, push fake apart + cos_sim = nn.functional.cosine_similarity( + outputs["visual_embed"], outputs["audio_embed"] + ) + # Real: cos_sim should be high (→ 1), Fake: should be low (→ -1) + # Loss: for real samples maximise similarity, for fake minimise + contrastive = (labels * (1 - cos_sim) + (1 - labels) * torch.clamp(cos_sim + 0.5, min=0)).mean() + + total = bce_loss + self.contrastive_weight * contrastive + + return { + "total": total, + "bce": bce_loss, + "contrastive": contrastive, + } diff --git a/training/phase2_coherence/preprocess_videos.py b/training/phase2_coherence/preprocess_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..6c35435a94a9371a6a35c0c99c8bb46f76f4fe5e --- /dev/null +++ b/training/phase2_coherence/preprocess_videos.py @@ -0,0 +1,324 @@ +""" +training/phase2_coherence/preprocess_videos.py + +Extracts lip-audio clip pairs from video datasets for Coherence engine training. + +Output: data/processed/coherence/{split}/{label}/{video_id}_{clip_idx}.npz + Each .npz contains: + lip_frames: (25, 96, 64, 3) uint8 + mfcc: (40, T) float32 + label: int 1=synced/real, 0=unsynced/fake + source: str dataset name + +Run: + python training/phase2_coherence/preprocess_videos.py \ + --ff_root /kaggle/input/faceforensics-in-compressed-videos \ + --dfdc_root /kaggle/input/deepfake-detection-challenge \ + --output_dir /kaggle/working/processed/coherence +""" + +from __future__ import annotations + +import argparse +import json +import logging +import multiprocessing as mp +import random +import time +from pathlib import Path + +import cv2 +import librosa +import numpy as np +from tqdm import tqdm + +# ── Logging ─────────────────────────────────────────────────────────────────── + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%H:%M:%S", +) +log = logging.getLogger(__name__) + +# ── Constants ───────────────────────────────────────────────────────────────── + +SAMPLE_RATE = 16_000 +N_MFCC = 40 +CLIP_FRAMES = 25 # 1 second at 25fps +LIP_H, LIP_W = 64, 96 +TARGET_FPS = 25 +MIN_VIDEO_HEIGHT = 240 + +face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + "haarcascade_frontalface_default.xml" +) + + +# ── Video processing ────────────────────────────────────────────────────────── + +def extract_audio(video_path: Path) -> np.ndarray | None: + """Extract mono audio at 16kHz using librosa.""" + try: + import subprocess, tempfile, os + # Extract audio to temp wav using ffmpeg + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: + tmp_path = tmp.name + result = subprocess.run( + ["ffmpeg", "-y", "-i", str(video_path), "-ac", "1", + "-ar", str(SAMPLE_RATE), tmp_path], + capture_output=True, timeout=60 + ) + if result.returncode != 0: + return None + audio, _ = librosa.load(tmp_path, sr=SAMPLE_RATE, mono=True) + os.unlink(tmp_path) + return audio + except Exception: + return None + + +def extract_lip_frames(cap: cv2.VideoCapture, start_frame: int) -> np.ndarray | None: + """ + Extract 25 lip ROI frames starting at start_frame. + Returns (25, 96, 64, 3) uint8 or None if detection fails. + """ + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + frames = [] + + for _ in range(CLIP_FRAMES): + ret, frame = cap.read() + if not ret: + return None + + if frame.shape[0] < MIN_VIDEO_HEIGHT: + return None + + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4) + + if len(faces) == 0: + frames.append(None) + continue + + # Take the largest face + x, y, w, h = max(faces, key=lambda f: f[2] * f[3]) + # Lip ROI: lower 1/3 of face bounding box + lip_y = y + int(h * 0.6) + lip_h_crop = int(h * 0.4) + lip_roi = frame[lip_y:lip_y + lip_h_crop, x:x + w] + + if lip_roi.size == 0: + frames.append(None) + continue + + lip_roi = cv2.resize(lip_roi, (LIP_W, LIP_H)) + lip_roi = cv2.cvtColor(lip_roi, cv2.COLOR_BGR2RGB) + frames.append(lip_roi) + + # Require ≤20% missing frames + missing = sum(1 for f in frames if f is None) + if missing > CLIP_FRAMES * 0.2: + return None + + # Fill missing frames with nearest neighbour + for i in range(len(frames)): + if frames[i] is None: + for j in range(i - 1, -1, -1): + if frames[j] is not None: + frames[i] = frames[j] + break + if frames[i] is None: + frames[i] = np.zeros((LIP_H, LIP_W, 3), dtype=np.uint8) + + return np.stack(frames, axis=0).astype(np.uint8) # (25, 64, 96, 3) + + +def process_video(args_tuple) -> list[dict]: + """Process a single video and return extracted clip metadata.""" + video_path, label, source, output_dir, max_clips, seed = args_tuple + rng = random.Random(seed) + clips = [] + + cap = cv2.VideoCapture(str(video_path)) + if not cap.isOpened(): + return clips + + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + fps = cap.get(cv2.CAP_PROP_FPS) or TARGET_FPS + + if total_frames < CLIP_FRAMES * 2: + cap.release() + return clips + + # Extract audio for the whole video + audio = extract_audio(video_path) + if audio is None: + cap.release() + return clips + + # Sample clip start positions + max_start = total_frames - CLIP_FRAMES + step = max(1, max_start // (max_clips * 2)) + candidates = list(range(0, max_start, step)) + rng.shuffle(candidates) + + video_id = video_path.stem + + for start_frame in candidates[:max_clips * 2]: + if len(clips) >= max_clips: + break + + lip_frames = extract_lip_frames(cap, start_frame) + if lip_frames is None: + continue + + # Corresponding audio segment + start_sec = start_frame / fps + audio_start = int(start_sec * SAMPLE_RATE) + audio_end = audio_start + SAMPLE_RATE # 1 second + if audio_end > len(audio): + continue + segment = audio[audio_start:audio_end] + + mfcc = librosa.feature.mfcc(y=segment, sr=SAMPLE_RATE, n_mfcc=N_MFCC) + + # Save + clip_idx = len(clips) + label_str = "real" if label == 1 else "fake" + out_path = output_dir / label_str / f"{video_id}_{clip_idx:04d}.npz" + out_path.parent.mkdir(parents=True, exist_ok=True) + + np.savez_compressed( + out_path, + lip_frames=lip_frames, + mfcc=mfcc.astype(np.float32), + label=np.array(label, dtype=np.int32), + source=np.array(source), + ) + clips.append({"path": str(out_path), "label": label}) + + cap.release() + return clips + + +# ── Dataset loaders ─────────────────────────────────────────────────────────── + +def collect_ff(ff_root: Path) -> list[tuple[Path, int, str]]: + """FaceForensics++: manipulated_sequences=fake, original_sequences=real.""" + videos = [] + for subfolder in ff_root.rglob("*.mp4"): + parts = [p.lower() for p in subfolder.parts] + label = 0 if "manipulated" in str(subfolder).lower() else 1 + videos.append((subfolder, label, "ff++")) + log.info(f"FF++: {len(videos)} videos") + return videos + + +def collect_dfdc(dfdc_root: Path) -> list[tuple[Path, int, str]]: + """DFDC: use metadata.json per chunk folder.""" + videos = [] + for metadata_file in dfdc_root.rglob("metadata.json"): + chunk_dir = metadata_file.parent + with open(metadata_file) as f: + meta = json.load(f) + for fname, info in meta.items(): + video_path = chunk_dir / fname + if not video_path.exists(): + continue + label = 1 if info.get("label", "FAKE") == "REAL" else 0 + videos.append((video_path, label, "dfdc")) + log.info(f"DFDC: {len(videos)} videos") + return videos + + +def collect_celebdf(celebdf_root: Path) -> list[tuple[Path, int, str]]: + """Celeb-DF v2: real/ and fake/ folders.""" + videos = [] + for video_path in celebdf_root.rglob("*.mp4"): + parts_lower = str(video_path).lower() + label = 0 if "fake" in parts_lower or "synthesis" in parts_lower else 1 + videos.append((video_path, label, "celebdf")) + log.info(f"CelebDF: {len(videos)} videos") + return videos + + +# ── Split assignment ────────────────────────────────────────────────────────── + +def assign_splits( + video_list: list[tuple[Path, int, str]], + seed: int = 42, + train_ratio: float = 0.80, + val_ratio: float = 0.10, +) -> dict[str, list[tuple[Path, int, str]]]: + rng = random.Random(seed) + shuffled = list(video_list) + rng.shuffle(shuffled) + n = len(shuffled) + n_train = int(n * train_ratio) + n_val = int(n * val_ratio) + return { + "train": shuffled[:n_train], + "val": shuffled[n_train:n_train + n_val], + "test": shuffled[n_train + n_val:], + } + + +# ── Main ───────────────────────────────────────────────────────────────────── + +def main(args: argparse.Namespace) -> None: + output_dir = Path(args.output_dir) + + all_videos: list[tuple[Path, int, str]] = [] + if args.ff_root and Path(args.ff_root).exists(): + all_videos += collect_ff(Path(args.ff_root)) + if args.dfdc_root and Path(args.dfdc_root).exists(): + all_videos += collect_dfdc(Path(args.dfdc_root)) + if args.celebdf_root and Path(args.celebdf_root).exists(): + all_videos += collect_celebdf(Path(args.celebdf_root)) + + if not all_videos: + log.error("No video datasets found — check your paths") + return + + log.info(f"Total videos: {len(all_videos)}") + splits = assign_splits(all_videos, seed=args.seed) + + total_clips = 0 + for split, videos in splits.items(): + split_dir = output_dir / split + split_dir.mkdir(parents=True, exist_ok=True) + + tasks = [ + (v, lbl, src, split_dir, args.max_clips, args.seed + i) + for i, (v, lbl, src) in enumerate(videos) + ] + + log.info(f"Processing {split} split ({len(tasks)} videos) ...") + with mp.Pool(args.workers) as pool: + results = list(tqdm( + pool.imap_unordered(process_video, tasks), + total=len(tasks), desc=split, + )) + + split_clips = sum(len(r) for r in results) + total_clips += split_clips + log.info(f" {split}: {split_clips} clips extracted") + + log.info(f"Done. Total clips: {total_clips}") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Preprocess videos for Coherence engine") + p.add_argument("--ff_root", default=None) + p.add_argument("--dfdc_root", default=None) + p.add_argument("--celebdf_root",default=None) + p.add_argument("--output_dir", default="data/processed/coherence") + p.add_argument("--workers", type=int, default=4) + p.add_argument("--max_clips", type=int, default=20) + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase2_coherence/train.py b/training/phase2_coherence/train.py new file mode 100644 index 0000000000000000000000000000000000000000..90422b99ffdc6a6c15d5f09d29946c92b644d8ab --- /dev/null +++ b/training/phase2_coherence/train.py @@ -0,0 +1,244 @@ +""" +training/phase2_coherence/train.py + +Training script for the Coherence engine (lip-audio sync). + +Kaggle usage: + !python training/phase2_coherence/train.py \ + --data_dir /kaggle/working/processed/coherence \ + --output_dir /kaggle/working/checkpoints/coherence \ + --epochs 25 --batch_size 16 --lr 1e-4 --amp +""" +from __future__ import annotations + +import argparse +import logging +import random +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import GradScaler, autocast +from torch.utils.data import DataLoader, Dataset +from sklearn.metrics import roc_auc_score, accuracy_score, f1_score +from tqdm import tqdm +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +import sys +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) +from training.phase2_coherence.model import CoherenceModel, CoherenceLoss + + +def setup_logging(output_dir: Path) -> None: + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = Path("training/logs") + log_dir.mkdir(parents=True, exist_ok=True) + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + handlers=[ + logging.StreamHandler(), + logging.FileHandler(log_dir / f"phase2_coherence_{ts}.log"), + ], + ) + +log = logging.getLogger(__name__) + + +class CoherenceDataset(Dataset): + def __init__(self, root_dir: Path, split: str): + self.samples = [] + split_dir = Path(root_dir) / split + for label_str, label in [("real", 1), ("fake", 0)]: + d = split_dir / label_str + if not d.exists(): + continue + for p in sorted(d.glob("*.npz")): + self.samples.append((p, label)) + log.info(f"CoherenceDataset [{split}]: {len(self.samples)} clips") + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int) -> tuple: + path, label = self.samples[idx] + data = np.load(path) + lip_frames = data["lip_frames"].astype(np.float32) / 255.0 # (25, H, W, 3) → [0,1] + mfcc = data["mfcc"].astype(np.float32) # (40, T) + + # lip_frames to (25, 3, 64, 96) + lip_tensor = torch.tensor(lip_frames).permute(0, 3, 1, 2) # (25, 3, H, W) + mfcc_tensor = torch.tensor(mfcc) + + # Pad/trim MFCC to fixed length 50 + T = mfcc_tensor.shape[1] + if T >= 50: + mfcc_tensor = mfcc_tensor[:, :50] + else: + mfcc_tensor = torch.nn.functional.pad(mfcc_tensor, (0, 50 - T)) + + return lip_tensor, mfcc_tensor, torch.tensor(label, dtype=torch.float32) + + +def train_epoch(model, loader, criterion, optimizer, scaler, device, args) -> dict: + model.train() + total_loss = 0.0 + all_preds, all_labels = [], [] + + for lip, mfcc, labels in tqdm(loader, desc="Train", leave=False): + lip, mfcc, labels = lip.to(device), mfcc.to(device), labels.to(device) + + with autocast(enabled=args.amp): + out = model(lip, mfcc) + losses = criterion(out, labels) + + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + total_loss += losses["total"].item() + preds = (out["sync_score"].detach().cpu().numpy() > 0.5).astype(int) + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy().astype(int)) + + return { + "loss": total_loss / len(loader), + "accuracy": accuracy_score(all_labels, all_preds), + } + + +@torch.no_grad() +def validate(model, loader, criterion, device, args, epoch: int, output_dir: Path) -> dict: + model.eval() + total_loss = 0.0 + all_scores, all_preds, all_labels = [], [], [] + + for lip, mfcc, labels in tqdm(loader, desc="Val", leave=False): + lip, mfcc, labels = lip.to(device), mfcc.to(device), labels.to(device) + with autocast(enabled=args.amp): + out = model(lip, mfcc) + losses = criterion(out, labels) + total_loss += losses["total"].item() + scores = out["sync_score"].cpu().numpy() + preds = (scores > 0.5).astype(int) + all_scores.extend(scores) + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy().astype(int)) + + auc = roc_auc_score(all_labels, all_scores) if len(set(all_labels)) > 1 else 0.5 + + # EER computation + from sklearn.metrics import roc_curve + fpr, tpr, thresholds = roc_curve(all_labels, all_scores) + eer_idx = np.argmin(np.abs(fpr - (1 - tpr))) + eer = float(fpr[eer_idx]) + + # Save ROC plot + plt.figure(figsize=(6, 4)) + plt.plot(fpr, tpr, label=f"AUC={auc:.3f}") + plt.xlabel("FPR"); plt.ylabel("TPR"); plt.title(f"ROC Epoch {epoch+1}") + plt.legend(); plt.tight_layout() + plt.savefig(output_dir / f"roc_epoch_{epoch+1:02d}.png") + plt.close() + + return { + "loss": total_loss / len(loader), + "auc": auc, + "eer": eer, + "accuracy": accuracy_score(all_labels, all_preds), + "f1": f1_score(all_labels, all_preds, zero_division=0), + } + + +def save_checkpoint(path, model, optimizer, scaler, epoch, val_auc, args): + torch.save({ + "epoch": epoch, + "model_state": model.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "val_auc": val_auc, + "config": vars(args), + }, path) + + +def main(args: argparse.Namespace) -> None: + setup_logging(Path(args.output_dir)) + torch.manual_seed(args.seed) + np.random.seed(args.seed) + random.seed(args.seed) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + train_ds = CoherenceDataset(args.data_dir, "train") + val_ds = CoherenceDataset(args.data_dir, "val") + train_dl = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=True) + val_dl = DataLoader(val_ds, batch_size=args.batch_size * 2, shuffle=False, + num_workers=args.workers, pin_memory=True) + + model = CoherenceModel().to(device) + criterion = CoherenceLoss(contrastive_weight=args.contrastive_weight) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, + weight_decay=args.weight_decay) + scaler = GradScaler(enabled=args.amp) + + best_auc = 0.0 + no_improve = 0 + + for epoch in range(args.epochs): + t0 = time.time() + train_m = train_epoch(model, train_dl, criterion, optimizer, scaler, device, args) + val_m = validate(model, val_dl, criterion, device, args, epoch, output_dir) + + log.info( + f"Epoch {epoch+1:02d}/{args.epochs} | " + f"train_loss={train_m['loss']:.4f} | " + f"val_auc={val_m['auc']:.4f} | EER={val_m['eer']:.3f} | " + f"{time.time()-t0:.0f}s" + ) + + save_checkpoint(output_dir / "latest.pt", model, optimizer, scaler, epoch, val_m["auc"], args) + + if val_m["auc"] > best_auc: + best_auc = val_m["auc"] + no_improve = 0 + save_checkpoint(output_dir / "best.pt", model, optimizer, scaler, epoch, best_auc, args) + log.info(f" New best AUC: {best_auc:.4f}") + else: + no_improve += 1 + if no_improve >= args.patience: + log.info(f"Early stopping at epoch {epoch+1}") + break + + log.info(f"Done. Best AUC: {best_auc:.4f}") + + +def parse_args(): + p = argparse.ArgumentParser(description="Train Coherence engine (Phase 2)") + p.add_argument("--data_dir", required=True) + p.add_argument("--output_dir", required=True) + p.add_argument("--epochs", type=int, default=25) + p.add_argument("--batch_size", type=int, default=16) + p.add_argument("--lr", type=float, default=1e-4) + p.add_argument("--weight_decay", type=float, default=1e-4) + p.add_argument("--contrastive_weight",type=float, default=0.1) + p.add_argument("--workers", type=int, default=4) + p.add_argument("--patience", type=int, default=5) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--amp", action="store_true", default=False) + p.add_argument("--resume", default=None) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase3_sstgnn/__init__.py b/training/phase3_sstgnn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase3_sstgnn/build_graph.py b/training/phase3_sstgnn/build_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..5092281f58e1f4b9c19d339bf73819f03910254b --- /dev/null +++ b/training/phase3_sstgnn/build_graph.py @@ -0,0 +1,161 @@ +""" +training/phase3_sstgnn/build_graph.py + +Converts a (T, L, 3) landmark sequence into a torch_geometric Data object. + +Graph structure: + Nodes: T × L = up to 4096 nodes + Features: (x, y, z, frame_idx/T, landmark_idx/L) → 5-dim + + Edges: + 1. SPATIAL — anatomically adjacent landmarks within each frame (weight 1.0) + 2. TEMPORAL — each landmark connects to same landmark in next 3 frames + (weight = exp(-|frame_diff|)) + 3. SELF — each node → itself (required for GAT residual) + +Copy to src/engines/sstgnn/graph_builder.py for inference use. +""" + +from __future__ import annotations + +import math +from typing import Tuple + +import numpy as np +import torch + +try: + from torch_geometric.data import Data + TORCH_GEOMETRIC_AVAILABLE = True +except ImportError: + TORCH_GEOMETRIC_AVAILABLE = False + + +# ── Anatomical adjacency (68-landmark subset, MediaPipe-derived) ────────────── + +def _build_adjacency_list() -> list[tuple[int, int]]: + """Edges between anatomically adjacent landmarks on 68-landmark face.""" + edges = [] + + # Jaw contour (0–16) + edges += [(i, i + 1) for i in range(16)] + + # Right eyebrow (17–21) + edges += [(i, i + 1) for i in range(17, 21)] + + # Left eyebrow (22–26) + edges += [(i, i + 1) for i in range(22, 26)] + + # Nose bridge (27–30) + edges += [(i, i + 1) for i in range(27, 30)] + + # Nose base (31–35) + edges += [(i, i + 1) for i in range(31, 35)] + edges += [(30, 35)] # connect bridge to base + + # Right eye (36–41) — closed loop + edges += [(36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 36)] + + # Left eye (42–47) — closed loop + edges += [(42, 43), (43, 44), (44, 45), (45, 46), (46, 47), (47, 42)] + + # Mouth outer (48–59) — closed loop + edges += [(i, i + 1) for i in range(48, 59)] + edges += [(59, 48)] + + # Mouth inner (60–67) — closed loop + edges += [(i, i + 1) for i in range(60, 67)] + edges += [(67, 60)] + + return edges + + +SPATIAL_EDGES = _build_adjacency_list() # ~51 pairs +TEMPORAL_WINDOW = 3 # connect to next N frames + + +def build_temporal_graph( + landmark_sequence: np.ndarray, # (T, L, 3) + label: int = -1, +) -> "Data": + """ + Build a spatio-temporal graph from a landmark sequence. + + Args: + landmark_sequence: (T, L, 3) — T frames, L landmarks, xyz normalized [0,1] + label: binary label for training (-1 for inference) + + Returns: + torch_geometric.data.Data with x, edge_index, edge_attr, y + """ + if not TORCH_GEOMETRIC_AVAILABLE: + raise ImportError("torch_geometric is required. See KAGGLE.md for install.") + + T, L, _ = landmark_sequence.shape + + # ── Node features ───────────────────────────────────────────────────────── + # 5-dim: (x, y, z, frame_idx/T, landmark_idx/L) + node_feats = [] + for t in range(T): + for l_idx in range(L): + x, y, z = landmark_sequence[t, l_idx] + node_feats.append([ + float(x), + float(y), + float(z), + t / max(T - 1, 1), + l_idx / max(L - 1, 1), + ]) + + node_tensor = torch.tensor(node_feats, dtype=torch.float32) # (T*L, 5) + + # ── Edge construction ───────────────────────────────────────────────────── + src_nodes, dst_nodes, weights = [], [], [] + + # Node index helper: frame t, landmark l → global node idx + def node_id(t: int, l: int) -> int: + return t * L + l + + # 1. SPATIAL edges — same frame, anatomically adjacent landmarks + for t in range(T): + for (i, j) in SPATIAL_EDGES: + if i < L and j < L: + u, v = node_id(t, i), node_id(t, j) + # Add both directions + src_nodes.extend([u, v]) + dst_nodes.extend([v, u]) + weights.extend([1.0, 1.0]) + + # 2. TEMPORAL edges — same landmark across adjacent frames + for t in range(T): + for dt in range(1, TEMPORAL_WINDOW + 1): + t2 = t + dt + if t2 >= T: + break + w = math.exp(-dt) # exponential decay + for l_idx in range(L): + u, v = node_id(t, l_idx), node_id(t2, l_idx) + src_nodes.extend([u, v]) + dst_nodes.extend([v, u]) + weights.extend([w, w]) + + # 3. SELF edges + for n in range(T * L): + src_nodes.append(n) + dst_nodes.append(n) + weights.append(1.0) + + edge_index = torch.tensor([src_nodes, dst_nodes], dtype=torch.long) + edge_attr = torch.tensor(weights, dtype=torch.float32).unsqueeze(1) # (E, 1) + + y = torch.tensor([label], dtype=torch.float32) if label >= 0 else None + + data = Data( + x=node_tensor, + edge_index=edge_index, + edge_attr=edge_attr, + ) + if y is not None: + data.y = y + + return data diff --git a/training/phase3_sstgnn/extract_landmarks.py b/training/phase3_sstgnn/extract_landmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..900d6f17f120b6dee1c7358a3c416361f2255c0c --- /dev/null +++ b/training/phase3_sstgnn/extract_landmarks.py @@ -0,0 +1,265 @@ +""" +training/phase3_sstgnn/extract_landmarks.py + +Extracts 68-landmark sequences from video datasets for SSTGNN training. + +Output: data/processed/sstgnn/{split}/{label}/{video_id}_{window_idx}.npy + Shape: (64, 68, 3) — 64 frames, 68 landmarks, xyz normalized [0,1] + +Key fixes applied vs original spec: + - min_detection_confidence=0.3 (was 0.5 — too strict for DFDC low-res) + - min_tracking_confidence=0.3 + - Skip frames where video height < 240px + +Run: + python training/phase3_sstgnn/extract_landmarks.py \ + --ff_root /kaggle/input/faceforensics-in-compressed-videos \ + --dfdc_root /kaggle/input/deepfake-detection-challenge \ + --output_dir /kaggle/working/processed/sstgnn \ + --workers 4 +""" + +from __future__ import annotations + +import argparse +import json +import logging +import multiprocessing as mp +import random +from pathlib import Path + +import cv2 +import numpy as np +import mediapipe as mp_lib +from tqdm import tqdm + +# ── Logging ─────────────────────────────────────────────────────────────────── + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%H:%M:%S", +) +log = logging.getLogger(__name__) + +# ── Constants ───────────────────────────────────────────────────────────────── + +WINDOW_FRAMES = 64 +WINDOW_STRIDE = 32 +NUM_LANDMARKS = 68 +MIN_VIDEO_HEIGHT = 240 + +# 68-landmark indices from MediaPipe 468-point mesh +# Selected to match the standard 68-point dlib subset +KEY_LANDMARKS = [ + 162, 234, 93, 58, 172, 136, 149, 148, 152, 377, 378, 365, 397, 288, 323, 454, + 389, 71, 63, 105, 66, 107, 336, 296, 334, 293, 301, 168, 197, 5, 4, 75, 97, + 2, 326, 305, 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380, 61, + 39, 37, 0, 267, 269, 291, 405, 314, 17, 84, 181, 78, 82, 13, 312, 308, 317, + 14, 87, +][:NUM_LANDMARKS] + + +def extract_landmarks_from_video(args_tuple) -> list[dict]: + """Worker function — processes a single video.""" + video_path, label, source, output_dir, seed = args_tuple + rng = random.Random(seed) + results = [] + + cap = cv2.VideoCapture(str(video_path)) + if not cap.isOpened(): + return results + + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + if total_frames < WINDOW_FRAMES: + cap.release() + return results + + # Initialise MediaPipe with lowered confidence thresholds + mp_face_mesh = mp_lib.solutions.face_mesh + face_mesh = mp_face_mesh.FaceMesh( + static_image_mode=False, + max_num_faces=1, + refine_landmarks=False, + min_detection_confidence=0.3, # FIX: was 0.5 + min_tracking_confidence=0.3, # FIX: was 0.5 + ) + + all_frame_landmarks = [] # per-frame landmark arrays or None + + frame_idx = 0 + while True: + ret, frame = cap.read() + if not ret: + break + + # Skip low-res frames + if frame.shape[0] < MIN_VIDEO_HEIGHT: + all_frame_landmarks.append(None) + frame_idx += 1 + continue + + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + result = face_mesh.process(rgb) + + if result.multi_face_landmarks: + lms = result.multi_face_landmarks[0].landmark + coords = np.array([ + [lms[i].x, lms[i].y, lms[i].z] + for i in KEY_LANDMARKS + ], dtype=np.float32) # (68, 3) + all_frame_landmarks.append(coords) + else: + all_frame_landmarks.append(None) + + frame_idx += 1 + + cap.release() + face_mesh.close() + + # Extract windows with stride + video_id = video_path.stem + label_str = "real" if label == 1 else "fake" + + for w_start in range(0, len(all_frame_landmarks) - WINDOW_FRAMES + 1, WINDOW_STRIDE): + window = all_frame_landmarks[w_start:w_start + WINDOW_FRAMES] + + # Skip windows with >20% missing detections + missing = sum(1 for f in window if f is None) + if missing > WINDOW_FRAMES * 0.2: + continue + + # Fill missing frames with nearest neighbour + filled = list(window) + for i in range(len(filled)): + if filled[i] is None: + for j in range(i - 1, -1, -1): + if filled[j] is not None: + filled[i] = filled[j] + break + if filled[i] is None: + filled[i] = np.zeros((NUM_LANDMARKS, 3), dtype=np.float32) + + seq = np.stack(filled, axis=0) # (64, 68, 3) + assert seq.shape == (WINDOW_FRAMES, NUM_LANDMARKS, 3) + + w_idx = len(results) + out_path = output_dir / label_str / f"{video_id}_{w_idx:04d}.npy" + out_path.parent.mkdir(parents=True, exist_ok=True) + np.save(out_path, seq) + results.append({"path": str(out_path), "label": label}) + + return results + + +# ── Dataset collectors (same as coherence) ─────────────────────────────────── + +def collect_ff(ff_root: Path) -> list[tuple[Path, int, str]]: + videos = [] + for p in ff_root.rglob("*.mp4"): + label = 0 if "manipulated" in str(p).lower() else 1 + videos.append((p, label, "ff++")) + log.info(f"FF++: {len(videos)} videos") + return videos + + +def collect_dfdc(dfdc_root: Path) -> list[tuple[Path, int, str]]: + videos = [] + for meta_file in dfdc_root.rglob("metadata.json"): + chunk_dir = meta_file.parent + with open(meta_file) as f: + meta = json.load(f) + for fname, info in meta.items(): + p = chunk_dir / fname + if not p.exists(): + continue + label = 1 if info.get("label", "FAKE") == "REAL" else 0 + videos.append((p, label, "dfdc")) + log.info(f"DFDC: {len(videos)} videos") + return videos + + +def collect_celebdf(celebdf_root: Path) -> list[tuple[Path, int, str]]: + videos = [] + for p in celebdf_root.rglob("*.mp4"): + label = 0 if "fake" in str(p).lower() else 1 + videos.append((p, label, "celebdf")) + log.info(f"CelebDF: {len(videos)} videos") + return videos + + +def assign_splits(videos, seed=42): + rng = random.Random(seed) + shuffled = list(videos) + rng.shuffle(shuffled) + n = len(shuffled) + n_train = int(n * 0.80) + n_val = int(n * 0.10) + return { + "train": shuffled[:n_train], + "val": shuffled[n_train:n_train + n_val], + "test": shuffled[n_train + n_val:], + } + + +# ── Main ───────────────────────────────────────────────────────────────────── + +def main(args: argparse.Namespace) -> None: + output_dir = Path(args.output_dir) + + all_videos = [] + if args.ff_root and Path(args.ff_root).exists(): + all_videos += collect_ff(Path(args.ff_root)) + if args.dfdc_root and Path(args.dfdc_root).exists(): + all_videos += collect_dfdc(Path(args.dfdc_root)) + if args.celebdf_root and Path(args.celebdf_root).exists(): + all_videos += collect_celebdf(Path(args.celebdf_root)) + + if not all_videos: + log.error("No videos found. Check dataset paths.") + return + + log.info(f"Total videos: {len(all_videos)}") + splits = assign_splits(all_videos, seed=args.seed) + + total_windows = 0 + failed = 0 + + for split, videos in splits.items(): + split_dir = output_dir / split + split_dir.mkdir(parents=True, exist_ok=True) + + tasks = [ + (v, lbl, src, split_dir, args.seed + i) + for i, (v, lbl, src) in enumerate(videos) + ] + + log.info(f"Extracting {split} landmarks ({len(tasks)} videos) ...") + with mp.Pool(args.workers) as pool: + results = list(tqdm( + pool.imap_unordered(extract_landmarks_from_video, tasks), + total=len(tasks), desc=split, + )) + + split_windows = sum(len(r) for r in results) + total_windows += split_windows + failed += sum(1 for r in results if len(r) == 0) + log.info(f" {split}: {split_windows} landmark windows") + + fail_pct = 100 * failed / max(len(all_videos), 1) + log.info(f"Done. Total windows: {total_windows} | Failed videos: {failed} ({fail_pct:.1f}%)") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Extract landmarks for SSTGNN") + p.add_argument("--ff_root", default=None) + p.add_argument("--dfdc_root", default=None) + p.add_argument("--celebdf_root",default=None) + p.add_argument("--output_dir", default="data/processed/sstgnn") + p.add_argument("--workers", type=int, default=4) + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase3_sstgnn/integrate.py b/training/phase3_sstgnn/integrate.py new file mode 100644 index 0000000000000000000000000000000000000000..de95b9710f1637769cae102fe245559fa12f0c34 --- /dev/null +++ b/training/phase3_sstgnn/integrate.py @@ -0,0 +1,86 @@ +""" +training/phase3_sstgnn/integrate.py + +Post-training integration for the SSTGNN engine. +State dict only — no TorchScript. + +Usage: + python training/phase3_sstgnn/integrate.py \ + --ckpt models/checkpoints/sstgnn/best.pt +""" +from __future__ import annotations + +import argparse +import shutil +from datetime import date +from pathlib import Path + +import torch + + +def main(args: argparse.Namespace) -> None: + ckpt_path = Path(args.ckpt) + assert ckpt_path.exists(), f"Checkpoint not found: {ckpt_path}" + + ckpt = torch.load(ckpt_path, map_location="cpu") + val_auc = ckpt.get("val_auc", "unknown") + epoch = ckpt.get("epoch", "unknown") + + print(f"\n=== SSTGNN checkpoint ===") + print(f" Epoch: {epoch}") + print(f" Val AUC: {val_auc}") + + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase3_sstgnn.model import SSTGNNModel + + model = SSTGNNModel() + key = "state_dict" if "state_dict" in ckpt else "model_state" + model.load_state_dict(ckpt[key]) + model.eval() + print(" Model loaded OK.") + print(" NOTE: SSTGNN uses state_dict only — no TorchScript export.") + + prod_dir = Path("models/checkpoints/sstgnn") + prod_dir.mkdir(parents=True, exist_ok=True) + prod_path = prod_dir / "production.pt" + + # Save in state_dict format for detector compatibility + torch.save({ + "state_dict": model.state_dict(), + "model_config": model.config, + "val_auc": val_auc, + "epoch": epoch, + }, prod_path) + + readme = prod_dir / "README.txt" + with open(readme, "w") as f: + f.write(f"GenAI-DeepDetect SSTGNN Engine Checkpoint\n") + f.write(f"Training date: {date.today()}\n") + f.write(f"Val AUC: {val_auc}\n") + f.write(f"Epoch: {epoch}\n") + f.write(f"Format: state_dict (load with model.load_state_dict)\n") + + print(f"\n Saved to: {prod_path}") + print(f""" +=== Integration steps === + +1. SSTGNNDetector auto-loads state_dict from: + models/checkpoints/sstgnn/best.pt + models/checkpoints/sstgnn/production.pt + +2. The detector uses load_state_dict — never torch.jit.load. + +3. Test: + python -c "from src.engines.sstgnn.detector import SSTGNNDetector; d = SSTGNNDetector(); print('Loaded')" +""") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--ckpt", default="models/checkpoints/sstgnn/best.pt") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase3_sstgnn/model.py b/training/phase3_sstgnn/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6992c3a4cc2a1d20386fa0e7fe01edec4aa65ce5 --- /dev/null +++ b/training/phase3_sstgnn/model.py @@ -0,0 +1,138 @@ +""" +training/phase3_sstgnn/model.py + +Spatio-Temporal Graph Neural Network (SSTGNN) for landmark-based deepfake detection. + +Architecture basis: SSTGNN (arXiv:2508.05526) + - 3-layer Graph Attention Network (GAT) over facial landmark temporal graphs + - Global mean + max pooling + - MLP classifier → anomaly logit + +IMPORTANT: Do NOT export this model with torch.jit.script or torch.jit.trace. + PyTorch Geometric's MessagePassing is not TorchScript-compatible. + Save as state dict only. See training/phase6_export/export_models.py. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + +try: + from torch_geometric.nn import GATConv, global_mean_pool, global_max_pool + from torch_geometric.data import Data + TORCH_GEOMETRIC_AVAILABLE = True +except ImportError: + TORCH_GEOMETRIC_AVAILABLE = False + + +class SSTGNNModel(nn.Module): + """ + Graph Attention Network over spatio-temporal facial landmark graphs. + + Input: torch_geometric.data.Data (or DataBatch) + x: (N, 5) node features: x, y, z, frame_idx/T, lm_idx/L + edge_index: (2, E) + edge_attr: (E, 1) + batch: (N,) batch assignment per node + + forward returns: + { + 'logit': Tensor (B,) — BCEWithLogitsLoss target + 'node_embeds': Tensor (N, 128) — for interpretability/attention viz + } + """ + + def __init__( + self, + in_channels: int = 5, + hidden_dim: int = 64, + heads: int = 4, + dropout: float = 0.3, + out_dropout: float = 0.4, + ) -> None: + super().__init__() + + if not TORCH_GEOMETRIC_AVAILABLE: + raise ImportError( + "torch_geometric is required for SSTGNNModel. " + "Install with: pip install torch_geometric" + ) + + # Layer 1: in_channels → hidden_dim*heads + self.gat1 = GATConv( + in_channels, hidden_dim, heads=heads, concat=True, + dropout=dropout, edge_dim=1, + ) + # Layer 2: hidden_dim*heads → hidden_dim*heads + self.gat2 = GATConv( + hidden_dim * heads, hidden_dim, heads=heads, concat=True, + dropout=dropout, edge_dim=1, + ) + # Layer 3: hidden_dim*heads → hidden_dim (summarise) + self.gat3 = GATConv( + hidden_dim * heads, hidden_dim, heads=1, concat=False, + dropout=dropout, edge_dim=1, + ) + + self.bn1 = nn.BatchNorm1d(hidden_dim * heads) + self.bn2 = nn.BatchNorm1d(hidden_dim * heads) + self.bn3 = nn.BatchNorm1d(hidden_dim) + + # Residual projection for dim matching + self.res_proj = nn.Linear(in_channels, hidden_dim * heads) + + # Classifier on pooled graph representation + pooled_dim = hidden_dim * 2 # mean + max + self.classifier = nn.Sequential( + nn.Linear(pooled_dim, 128), + nn.LayerNorm(128), + nn.ReLU(inplace=True), + nn.Dropout(out_dropout), + nn.Linear(128, 64), + nn.ReLU(inplace=True), + nn.Linear(64, 1), # logit + ) + + def forward(self, data) -> dict[str, torch.Tensor]: + x, edge_index, edge_attr, batch = ( + data.x, data.edge_index, data.edge_attr, data.batch + ) + + # Layer 1 + res = self.res_proj(x) + out = self.gat1(x, edge_index, edge_attr) + out = F.elu(self.bn1(out + res)) + + # Layer 2 + res2 = out + out = self.gat2(out, edge_index, edge_attr) + out = F.elu(self.bn2(out + res2)) + + # Layer 3 + out = self.gat3(out, edge_index, edge_attr) + out = F.elu(self.bn3(out)) # (N, hidden_dim) + + node_embeds = out # save for interpretability + + # Global pooling + g_mean = global_mean_pool(out, batch) # (B, hidden_dim) + g_max = global_max_pool(out, batch) # (B, hidden_dim) + pooled = torch.cat([g_mean, g_max], dim=1) # (B, hidden_dim*2) + + logit = self.classifier(pooled).squeeze(-1) # (B,) + + return { + "logit": logit, + "node_embeds": node_embeds, + } + + @property + def config(self) -> dict: + return { + "in_channels": 5, + "hidden_dim": 64, + "heads": 4, + "architecture": "GAT3L-globalpool", + } diff --git a/training/phase3_sstgnn/train.py b/training/phase3_sstgnn/train.py new file mode 100644 index 0000000000000000000000000000000000000000..520155fe7a798073e703cf3fa516a24d4c09e00b --- /dev/null +++ b/training/phase3_sstgnn/train.py @@ -0,0 +1,282 @@ +""" +training/phase3_sstgnn/train.py + +Training script for SSTGNNModel using PyTorch Geometric DataLoader. + +Kaggle usage (T4x2, no --amp): + !python training/phase3_sstgnn/train.py \ + --data_dir /kaggle/working/processed/sstgnn \ + --output_dir /kaggle/working/checkpoints/sstgnn \ + --epochs 40 --batch_size 8 --lr 5e-4 + +Note: Do NOT use --amp for SSTGNN. Mixed precision causes NaN in some +GAT configurations. Gradient clipping is always applied (max_norm=1.0). +""" + +from __future__ import annotations + +import argparse +import logging +import random +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from sklearn.metrics import roc_auc_score, accuracy_score, f1_score +from tqdm import tqdm + +try: + from torch_geometric.data import Data, Dataset as PyGDataset + from torch_geometric.loader import DataLoader as PyGDataLoader + TORCH_GEOMETRIC_AVAILABLE = True +except ImportError: + TORCH_GEOMETRIC_AVAILABLE = False + raise ImportError("torch_geometric required. See KAGGLE.md Cell 1.") + +import sys +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) +from training.phase3_sstgnn.model import SSTGNNModel +from training.phase3_sstgnn.build_graph import build_temporal_graph + +# ── Logging ─────────────────────────────────────────────────────────────────── + +def setup_logging() -> None: + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = Path("training/logs") + log_dir.mkdir(parents=True, exist_ok=True) + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(message)s", + handlers=[ + logging.StreamHandler(), + logging.FileHandler(log_dir / f"phase3_sstgnn_{ts}.log"), + ], + ) + +log = logging.getLogger(__name__) + + +# ── Dataset ─────────────────────────────────────────────────────────────────── + +class SSTGNNDataset(PyGDataset): + def __init__(self, root_dir: Path, split: str): + self.split_dir = Path(root_dir) / split + self.samples: list[tuple[Path, int]] = [] + + for label_str, label in [("real", 0), ("fake", 1)]: + d = self.split_dir / label_str + if not d.exists(): + continue + for p in sorted(d.glob("*.npy")): + self.samples.append((p, label)) + + log.info(f"SSTGNNDataset [{split}]: {len(self.samples)} windows") + super().__init__() + + def len(self) -> int: + return len(self.samples) + + def get(self, idx: int) -> Data: + path, label = self.samples[idx] + seq = np.load(path) # (64, 68, 3) + + # Validate shape + assert seq.shape == (64, 68, 3) or len(seq.shape) == 3, \ + f"Unexpected shape {seq.shape} for {path}" + if len(seq) < 5: + raise ValueError(f"Too few frames in {path}") + + return build_temporal_graph(seq, label=label) + + +# ── Training ────────────────────────────────────────────────────────────────── + +def train_epoch(model, loader, criterion, optimizer, device) -> dict: + model.train() + total_loss = 0.0 + all_preds, all_labels = [], [] + + for batch in tqdm(loader, desc="Train", leave=False): + batch = batch.to(device) + optimizer.zero_grad() + + outputs = model(batch) + logits = outputs["logit"] + labels = batch.y.squeeze(-1) + + loss = criterion(logits, labels) + loss.backward() + + # Always clip gradients for GNNs + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + + total_loss += loss.item() + preds = (torch.sigmoid(logits) > 0.5).long().cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().long().numpy()) + + return { + "loss": total_loss / len(loader), + "accuracy": accuracy_score(all_labels, all_preds), + } + + +@torch.no_grad() +def validate(model, loader, criterion, device) -> dict: + model.eval() + total_loss = 0.0 + all_preds, all_labels, all_probs = [], [], [] + + for batch in tqdm(loader, desc="Val", leave=False): + batch = batch.to(device) + outputs = model(batch) + logits = outputs["logit"] + labels = batch.y.squeeze(-1) + + loss = criterion(logits, labels) + total_loss += loss.item() + + probs = torch.sigmoid(logits).cpu().numpy() + preds = (probs > 0.5).astype(int) + all_probs.extend(probs) + all_preds.extend(preds) + all_labels.extend(labels.cpu().long().numpy()) + + auc = roc_auc_score(all_labels, all_probs) if len(set(all_labels)) > 1 else 0.5 + return { + "loss": total_loss / len(loader), + "auc": auc, + "accuracy": accuracy_score(all_labels, all_preds), + "f1": f1_score(all_labels, all_preds, zero_division=0), + } + + +def save_checkpoint(path, model, optimizer, epoch, val_auc, args) -> None: + torch.save({ + "epoch": epoch, + "model_state": model.state_dict(), + "optimizer": optimizer.state_dict(), + "val_auc": val_auc, + "config": vars(args), + "model_config": model.config, + }, path) + + +def main(args: argparse.Namespace) -> None: + setup_logging() + torch.manual_seed(args.seed) + np.random.seed(args.seed) + random.seed(args.seed) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + log.info(f"Device: {device}") + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # ── Datasets ────────────────────────────────────────────────────────────── + train_ds = SSTGNNDataset(args.data_dir, "train") + val_ds = SSTGNNDataset(args.data_dir, "val") + + # Compute pos_weight for class imbalance + labels = [s[1] for s in train_ds.samples] + n_real = labels.count(0) + n_fake = labels.count(1) + pos_weight = torch.tensor([n_real / max(n_fake, 1)], dtype=torch.float32).to(device) + + train_loader = PyGDataLoader( + train_ds, batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, + ) + val_loader = PyGDataLoader( + val_ds, batch_size=args.batch_size * 2, shuffle=False, + num_workers=args.workers, + ) + + # ── Model ───────────────────────────────────────────────────────────────── + model = SSTGNNModel().to(device) + criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.lr, weight_decay=args.weight_decay + ) + + if args.scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + else: + scheduler = torch.optim.lr_scheduler.StepLR( + optimizer, step_size=10, gamma=0.5 + ) + + # ── Resume ──────────────────────────────────────────────────────────────── + start_epoch = 0 + best_auc = 0.0 + no_improve = 0 + + if args.resume and Path(args.resume).exists(): + ckpt = torch.load(args.resume, map_location=device) + model.load_state_dict(ckpt["model_state"]) + optimizer.load_state_dict(ckpt["optimizer"]) + start_epoch = ckpt["epoch"] + 1 + best_auc = ckpt["val_auc"] + log.info(f"Resumed from {args.resume} — epoch {start_epoch}, AUC={best_auc:.4f}") + + # ── Training loop ───────────────────────────────────────────────────────── + log.info(f"Training SSTGNN for {args.epochs} epochs on {device}") + for epoch in range(start_epoch, args.epochs): + t0 = time.time() + + train_m = train_epoch(model, train_loader, criterion, optimizer, device) + val_m = validate(model, val_loader, criterion, device) + scheduler.step() + + elapsed = time.time() - t0 + log.info( + f"Epoch {epoch+1:02d}/{args.epochs} | " + f"train_loss={train_m['loss']:.4f} | " + f"val_auc={val_m['auc']:.4f} | " + f"val_acc={val_m['accuracy']:.4f} | " + f"lr={optimizer.param_groups[0]['lr']:.2e} | " + f"{elapsed:.0f}s" + ) + + save_checkpoint(output_dir / "latest.pt", model, optimizer, epoch, val_m["auc"], args) + + if val_m["auc"] > best_auc: + best_auc = val_m["auc"] + no_improve = 0 + save_checkpoint(output_dir / "best.pt", model, optimizer, epoch, best_auc, args) + log.info(f" New best AUC: {best_auc:.4f}") + else: + no_improve += 1 + if no_improve >= args.patience: + log.info(f"Early stopping at epoch {epoch+1}") + break + + log.info(f"Training complete. Best val AUC: {best_auc:.4f}") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Train SSTGNN engine (Phase 3)") + p.add_argument("--data_dir", required=True) + p.add_argument("--output_dir", required=True) + p.add_argument("--epochs", type=int, default=40) + p.add_argument("--batch_size", type=int, default=8) + p.add_argument("--lr", type=float, default=5e-4) + p.add_argument("--weight_decay",type=float, default=5e-4) + p.add_argument("--scheduler", default="cosine", choices=["cosine", "step"]) + p.add_argument("--workers", type=int, default=2) + p.add_argument("--patience", type=int, default=8) + p.add_argument("--seed", type=int, default=42) + # Note: --amp intentionally omitted — too risky for GNNs + p.add_argument("--resume", default=None) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase4_fusion/__init__.py b/training/phase4_fusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase4_fusion/calibrate_weights.py b/training/phase4_fusion/calibrate_weights.py new file mode 100644 index 0000000000000000000000000000000000000000..3a3d4aff6d219ec57b36546dd6d6fe1348cf3129 --- /dev/null +++ b/training/phase4_fusion/calibrate_weights.py @@ -0,0 +1,155 @@ +""" +training/phase4_fusion/calibrate_weights.py + +Calibrates fusion layer weights by computing AUC for each engine on a shared +held-out validation set, then computing softmax weights. + +Updates src/fusion/fuser.py WEIGHTS dict with calibrated values. + +Run: + python training/phase4_fusion/calibrate_weights.py \ + --fp_ckpt models/checkpoints/fingerprint/best.pt \ + --co_ckpt models/checkpoints/coherence/best.pt \ + --sg_ckpt models/checkpoints/sstgnn/best.pt \ + --output models/fusion/weights.json +""" + +from __future__ import annotations + +import argparse +import json +import logging +from pathlib import Path + +import numpy as np +import torch +from sklearn.metrics import roc_auc_score + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + + +def softmax(values: list[float]) -> list[float]: + exp_v = [np.exp(v) for v in values] + total = sum(exp_v) + return [v / total for v in exp_v] + + +def load_fingerprint_model(ckpt_path: Path): + """Load trained fingerprint model.""" + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase1_fingerprint.model import build_fingerprint_model + + model = build_fingerprint_model(pretrained=False) + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + return model + + +def load_coherence_model(ckpt_path: Path): + from training.phase2_coherence.model import CoherenceModel + + model = CoherenceModel() + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + return model + + +def load_sstgnn_model(ckpt_path: Path): + from training.phase3_sstgnn.model import SSTGNNModel + + model = SSTGNNModel() + ckpt = torch.load(ckpt_path, map_location="cpu") + model.load_state_dict(ckpt["model_state"]) + model.eval() + return model + + +def estimate_auc_from_checkpoint(ckpt_path: Path) -> float: + """Read val_auc from checkpoint metadata as proxy.""" + ckpt = torch.load(ckpt_path, map_location="cpu") + return float(ckpt.get("val_auc", 0.85)) + + +def main(args: argparse.Namespace) -> None: + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Retrieve val AUC from each checkpoint + fp_auc = co_auc = sg_auc = None + + if args.fp_ckpt and Path(args.fp_ckpt).exists(): + fp_auc = estimate_auc_from_checkpoint(Path(args.fp_ckpt)) + log.info(f"Fingerprint val AUC: {fp_auc:.4f}") + else: + fp_auc = 0.92 + log.warning(f"Fingerprint checkpoint not found — using default AUC {fp_auc}") + + if args.co_ckpt and Path(args.co_ckpt).exists(): + co_auc = estimate_auc_from_checkpoint(Path(args.co_ckpt)) + log.info(f"Coherence val AUC: {co_auc:.4f}") + else: + co_auc = 0.88 + log.warning(f"Coherence checkpoint not found — using default AUC {co_auc}") + + if args.sg_ckpt and Path(args.sg_ckpt).exists(): + sg_auc = estimate_auc_from_checkpoint(Path(args.sg_ckpt)) + log.info(f"SSTGNN val AUC: {sg_auc:.4f}") + else: + sg_auc = 0.85 + log.warning(f"SSTGNN checkpoint not found — using default AUC {sg_auc}") + + # Compute softmax weights + auc_scores = [fp_auc, co_auc, sg_auc] + weights = softmax(auc_scores) + + result = { + "fingerprint": { + "val_auc": fp_auc, + "weight": round(weights[0], 4), + }, + "coherence": { + "val_auc": co_auc, + "weight": round(weights[1], 4), + }, + "sstgnn": { + "val_auc": sg_auc, + "weight": round(weights[2], 4), + }, + "note": ( + "For image inputs: weight fingerprint=1.0, coherence=0.0, sstgnn=0.0. " + "For video inputs: use these AUC-softmax weights." + ), + } + + with open(output_path, "w") as f: + json.dump(result, f, indent=2) + + print("\n=== Fusion calibration ===") + print(f"{'Engine':<14} | {'Val AUC':>8} | {'Weight':>8}") + print("-" * 36) + for engine, data in result.items(): + if isinstance(data, dict) and "val_auc" in data: + print(f"{engine:<14} | {data['val_auc']:>8.4f} | {data['weight']:>8.4f}") + + print(f"\nWeights saved to: {output_path}") + print("\nCopy these values to src/fusion/fuser.py WEIGHTS dict:") + print(f" fingerprint: {result['fingerprint']['weight']}") + print(f" coherence: {result['coherence']['weight']}") + print(f" sstgnn: {result['sstgnn']['weight']}") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Calibrate fusion weights") + p.add_argument("--fp_ckpt", default=None, help="Fingerprint best.pt") + p.add_argument("--co_ckpt", default=None, help="Coherence best.pt") + p.add_argument("--sg_ckpt", default=None, help="SSTGNN best.pt") + p.add_argument("--output", default="models/fusion/weights.json") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase4_fusion/test_fusion_weights.py b/training/phase4_fusion/test_fusion_weights.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7d4f70585cd8a3d59c5160a281ebb0084cd59f --- /dev/null +++ b/training/phase4_fusion/test_fusion_weights.py @@ -0,0 +1,72 @@ +""" +training/phase4_fusion/test_fusion_weights.py + +Smoke test for calibrate_weights.py output. +Run after calibration to verify the weights file is valid. + +Usage: + python training/phase4_fusion/test_fusion_weights.py \ + --weights models/fusion/weights.json +""" +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + + +def main(args: argparse.Namespace) -> None: + path = Path(args.weights) + if not path.exists(): + print(f"FAIL: weights file not found: {path}") + sys.exit(1) + + with open(path) as f: + data = json.load(f) + + errors = [] + + for engine in ["fingerprint", "coherence", "sstgnn"]: + if engine not in data: + errors.append(f"Missing engine: {engine}") + continue + w = data[engine].get("weight") + if w is None: + errors.append(f"{engine}: missing 'weight' key") + elif not (0.0 <= w <= 1.0): + errors.append(f"{engine}: weight {w} out of [0,1]") + auc = data[engine].get("val_auc") + if auc is not None and not (0.0 <= auc <= 1.0): + errors.append(f"{engine}: val_auc {auc} out of [0,1]") + + # Weights should roughly sum to 1 + total_w = sum(data[e].get("weight", 0) for e in ["fingerprint", "coherence", "sstgnn"] if e in data) + if not (0.95 <= total_w <= 1.05): + errors.append(f"Weights sum {total_w:.4f} is not close to 1.0") + + if errors: + print("FAIL:") + for e in errors: + print(f" {e}") + sys.exit(1) + + print(f"PASS: {path}") + print(f"\n {'Engine':<14} {'Val AUC':>8} {'Weight':>8}") + print(f" {'-'*34}") + for engine in ["fingerprint", "coherence", "sstgnn"]: + if engine in data: + auc = data[engine].get("val_auc", "?") + w = data[engine].get("weight", "?") + print(f" {engine:<14} {auc!s:>8} {w!s:>8}") + print(f"\n Sum of weights: {total_w:.4f}") + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--weights", default="models/fusion/weights.json") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase5_eval/__init__.py b/training/phase5_eval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase5_eval/evaluate_all.py b/training/phase5_eval/evaluate_all.py new file mode 100644 index 0000000000000000000000000000000000000000..8642daa12fcf52605971c2a1bb2857e86d498e68 --- /dev/null +++ b/training/phase5_eval/evaluate_all.py @@ -0,0 +1,180 @@ +""" +training/phase5_eval/evaluate_all.py + +Comprehensive evaluation of the full pipeline on TEST splits only. + +Outputs: + models/evaluation_report.json + models/evaluation_plots/ + models/thresholds.json ← optimal F1 thresholds per engine + +Run: + python training/phase5_eval/evaluate_all.py +""" +from __future__ import annotations + +import argparse +import json +import logging +from pathlib import Path + +import numpy as np +import torch +from sklearn.metrics import ( + roc_auc_score, average_precision_score, f1_score, + accuracy_score, confusion_matrix, classification_report, + roc_curve, precision_recall_curve +) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +PLOTS_DIR = Path("models/evaluation_plots") +REPORT_OUT = Path("models/evaluation_report.json") +THRESH_OUT = Path("models/thresholds.json") + + +# ── Threshold optimisation ──────────────────────────────────────────────────── + +def optimal_f1_threshold(y_true, y_scores) -> tuple[float, float]: + """Returns (threshold, f1) that maximise F1.""" + precision, recall, thresholds = precision_recall_curve(y_true, y_scores) + f1_scores = 2 * precision * recall / (precision + recall + 1e-8) + best_idx = np.argmax(f1_scores[:-1]) + return float(thresholds[best_idx]), float(f1_scores[best_idx]) + + +def compute_eer(y_true, y_scores) -> float: + """Equal Error Rate.""" + fpr, tpr, _ = roc_curve(y_true, y_scores) + eer_idx = np.argmin(np.abs(fpr - (1 - tpr))) + return float(fpr[eer_idx]) + + +def evaluate_engine( + engine_name: str, + y_true: list[int], + y_scores: list[float], + val_y_true: list[int], + val_y_scores: list[float], +) -> dict: + """Compute full metrics for one engine.""" + opt_threshold, _ = optimal_f1_threshold(val_y_true, val_y_scores) + y_pred = (np.array(y_scores) >= opt_threshold).astype(int) + + auc_roc = roc_auc_score(y_true, y_scores) if len(set(y_true)) > 1 else 0.5 + auc_pr = average_precision_score(y_true, y_scores) if len(set(y_true)) > 1 else 0.5 + eer = compute_eer(y_true, y_scores) if len(set(y_true)) > 1 else 0.5 + + metrics = { + "engine": engine_name, + "n_samples": len(y_true), + "auc_roc": round(auc_roc, 4), + "auc_pr": round(auc_pr, 4), + "eer": round(eer, 4), + "optimal_threshold": round(opt_threshold, 4), + "f1_at_threshold": round(f1_score(y_true, y_pred, zero_division=0), 4), + "accuracy": round(accuracy_score(y_true, y_pred), 4), + "confusion_matrix": confusion_matrix(y_true, y_pred).tolist(), + } + + log.info( + f" {engine_name:12s} | AUC={auc_roc:.4f} | AP={auc_pr:.4f} | " + f"EER={eer:.3f} | F1={metrics['f1_at_threshold']:.4f} | " + f"thresh={opt_threshold:.3f}" + ) + return metrics + + +# ── Fingerprint evaluation ──────────────────────────────────────────────────── + +def evaluate_fingerprint(ckpt_path: Path, data_dir: Path) -> tuple[dict, float]: + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase1_fingerprint.model import build_fingerprint_model + from training.phase1_fingerprint.train import FingerprintDataset, get_val_transform + + log.info("Evaluating Fingerprint engine ...") + model = build_fingerprint_model(pretrained=False) + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = model.to(device) + + from torch.utils.data import DataLoader + test_ds = FingerprintDataset(data_dir, "test", get_val_transform()) + val_ds = FingerprintDataset(data_dir, "val", get_val_transform()) + test_dl = DataLoader(test_ds, batch_size=64, shuffle=False, num_workers=2) + val_dl = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=2) + + def run(loader): + scores, labels = [], [] + with torch.no_grad(): + for batch in loader: + imgs = batch["image"].to(device) + out = model(imgs) + probs = torch.softmax(out["binary_logits"], 1)[:, 1].cpu().numpy() + scores.extend(probs) + labels.extend(batch["label"].numpy()) + return labels, scores + + val_labels, val_scores = run(val_dl) + test_labels, test_scores = run(test_dl) + metrics = evaluate_engine("fingerprint", test_labels, test_scores, val_labels, val_scores) + return metrics, metrics["optimal_threshold"] + + +# ── Main ───────────────────────────────────────────────────────────────────── + +def main(args: argparse.Namespace) -> None: + PLOTS_DIR.mkdir(parents=True, exist_ok=True) + Path("models").mkdir(parents=True, exist_ok=True) + + report = {"engines": {}, "fused": {}} + thresholds: dict[str, float] = {} + + fp_ckpt = Path(args.fp_ckpt) if args.fp_ckpt else Path("models/checkpoints/fingerprint/best.pt") + + if fp_ckpt.exists() and args.fp_data: + metrics, thresh = evaluate_fingerprint(fp_ckpt, Path(args.fp_data)) + report["engines"]["fingerprint"] = metrics + thresholds["fingerprint"] = thresh + else: + log.warning("Skipping fingerprint evaluation (checkpoint or data not found)") + + # Save thresholds + with open(THRESH_OUT, "w") as f: + json.dump(thresholds, f, indent=2) + log.info(f"Thresholds saved to {THRESH_OUT}") + + # Save report + with open(REPORT_OUT, "w") as f: + json.dump(report, f, indent=2) + log.info(f"Report saved to {REPORT_OUT}") + + # Summary table + print("\n=== Evaluation Summary ===") + print(f"{'Engine':<14} {'AUC-ROC':>8} {'AUC-PR':>8} {'EER':>6} {'F1':>6} {'Thresh':>8}") + print("-" * 52) + for name, m in report["engines"].items(): + print( + f"{name:<14} {m['auc_roc']:>8.4f} {m['auc_pr']:>8.4f} " + f"{m['eer']:>6.3f} {m['f1_at_threshold']:>6.4f} {m['optimal_threshold']:>8.4f}" + ) + + +def parse_args(): + p = argparse.ArgumentParser(description="Evaluate all engines on test splits") + p.add_argument("--fp_ckpt", default=None, help="Fingerprint best.pt") + p.add_argument("--fp_data", default=None, help="Fingerprint processed data dir") + p.add_argument("--co_ckpt", default=None) + p.add_argument("--co_data", default=None) + p.add_argument("--sg_ckpt", default=None) + p.add_argument("--sg_data", default=None) + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase5_eval/threshold_tuning.py b/training/phase5_eval/threshold_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..e3e45d7f7b2eacdd84a3d93a3a09cd74ea0ae23e --- /dev/null +++ b/training/phase5_eval/threshold_tuning.py @@ -0,0 +1,101 @@ +""" +training/phase5_eval/threshold_tuning.py + +Standalone threshold tuning script. Finds optimal F1 threshold for each engine +on the validation set and writes models/thresholds.json. + +Run after training all engines: + python training/phase5_eval/threshold_tuning.py \ + --fp_ckpt models/checkpoints/fingerprint/best.pt \ + --fp_data data/processed/fingerprint +""" +from __future__ import annotations + +import argparse +import json +import logging +from pathlib import Path + +import numpy as np +import torch +from sklearn.metrics import precision_recall_curve + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + + +def find_optimal_threshold(y_true: list, y_scores: list) -> tuple[float, float]: + """Returns (threshold, f1) maximising F1 on the given set.""" + precision, recall, thresholds = precision_recall_curve(y_true, y_scores) + f1 = 2 * precision * recall / (precision + recall + 1e-8) + best = int(np.argmax(f1[:-1])) + return float(thresholds[best]), float(f1[best]) + + +def run_fingerprint(ckpt_path: Path, data_dir: Path) -> float: + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase1_fingerprint.model import build_fingerprint_model + from training.phase1_fingerprint.train import FingerprintDataset, get_val_transform + from torch.utils.data import DataLoader + + model = build_fingerprint_model(pretrained=False) + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + + ds = FingerprintDataset(data_dir, "val", get_val_transform()) + dl = DataLoader(ds, batch_size=64, shuffle=False, num_workers=2) + + all_probs, all_labels = [], [] + with torch.no_grad(): + for batch in dl: + out = model(batch["image"]) + probs = torch.softmax(out["binary_logits"], 1)[:, 1].numpy() + all_probs.extend(probs) + all_labels.extend(batch["label"].numpy()) + + thresh, f1 = find_optimal_threshold(all_labels, all_probs) + log.info(f"Fingerprint: optimal threshold={thresh:.4f}, F1={f1:.4f}") + return thresh + + +def main(args: argparse.Namespace) -> None: + thresholds: dict[str, float] = {} + + if args.fp_ckpt and Path(args.fp_ckpt).exists() and args.fp_data: + thresholds["fingerprint"] = run_fingerprint( + Path(args.fp_ckpt), Path(args.fp_data) + ) + else: + log.warning("Fingerprint: checkpoint or data not provided — using default 0.5") + thresholds["fingerprint"] = 0.5 + + # Coherence and SSTGNN default to 0.5 until video eval data is available + thresholds["coherence"] = args.co_threshold + thresholds["sstgnn"] = args.sg_threshold + + out_path = Path(args.output) + out_path.parent.mkdir(parents=True, exist_ok=True) + with open(out_path, "w") as f: + json.dump(thresholds, f, indent=2) + + print(f"\n=== Optimal thresholds ===") + for engine, thresh in thresholds.items(): + print(f" {engine:<14}: {thresh:.4f}") + print(f"\nSaved to: {out_path}") + print("Detectors will auto-load this file on startup.") + + +def parse_args(): + p = argparse.ArgumentParser(description="Find optimal F1 thresholds") + p.add_argument("--fp_ckpt", default=None) + p.add_argument("--fp_data", default=None) + p.add_argument("--co_threshold", type=float, default=0.5) + p.add_argument("--sg_threshold", type=float, default=0.5) + p.add_argument("--output", default="models/thresholds.json") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase6_export/__init__.py b/training/phase6_export/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/training/phase6_export/export_models.py b/training/phase6_export/export_models.py new file mode 100644 index 0000000000000000000000000000000000000000..11c1e3aa812f21790874a3eaa19a55f99167e2ef --- /dev/null +++ b/training/phase6_export/export_models.py @@ -0,0 +1,191 @@ +""" +training/phase6_export/export_models.py + +Exports all three trained models to models/production/. + +IMPORTANT fixes vs original TRAINING.md spec: + - Fingerprint: torch.jit.trace (works fine) + - Coherence: torch.jit.trace (works fine) + - SSTGNN: state dict ONLY — no TorchScript. + PyTorch Geometric's MessagePassing is not TorchScript-compatible. + +Run: + python training/phase6_export/export_models.py \ + --fp_ckpt models/checkpoints/fingerprint/best.pt \ + --co_ckpt models/checkpoints/coherence/best.pt \ + --sg_ckpt models/checkpoints/sstgnn/best.pt +""" + +from __future__ import annotations + +import argparse +import json +import logging +from datetime import date +from pathlib import Path + +import torch + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +PRODUCTION_DIR = Path("models/production") + + +def export_fingerprint(ckpt_path: Path) -> None: + """Export fingerprint model as TorchScript (trace).""" + import sys + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + from training.phase1_fingerprint.model import build_fingerprint_model + + log.info("Exporting Fingerprint model ...") + model = build_fingerprint_model(pretrained=False) + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + + # Verify forward pass + dummy = torch.randn(1, 3, 224, 224) + with torch.no_grad(): + out = model(dummy) + assert "binary_logits" in out + assert out["binary_logits"].shape == (1, 2) + assert out["generator_logits"].shape == (1, 8) + log.info(" Forward pass OK") + + # TorchScript trace + traced = torch.jit.trace(model, dummy) + out_path = PRODUCTION_DIR / "fingerprint.pt" + torch.jit.save(traced, str(out_path)) + log.info(f" Saved TorchScript to {out_path}") + + # Verify traced model + traced_out = traced(dummy) + max_diff = (traced_out["binary_logits"] - out["binary_logits"]).abs().max().item() + assert max_diff < 1e-4, f"TorchScript diff too large: {max_diff}" + log.info(f" TorchScript verification OK (max diff = {max_diff:.2e})") + + return { + "val_auc": float(ckpt.get("val_auc", 0.0)), + "checkpoint_epoch": int(ckpt.get("epoch", -1)), + "export_format": "torchscript_trace", + } + + +def export_coherence(ckpt_path: Path) -> None: + """Export coherence model as TorchScript (trace).""" + from training.phase2_coherence.model import CoherenceModel + + log.info("Exporting Coherence model ...") + model = CoherenceModel() + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + model.load_state_dict(ckpt["model_state"]) + model.eval() + + dummy_lips = torch.randn(1, 25, 3, 64, 96) + dummy_mfcc = torch.randn(1, 40, 50) + + with torch.no_grad(): + out = model(dummy_lips, dummy_mfcc) + assert "sync_score" in out + log.info(" Forward pass OK") + + traced = torch.jit.trace(model, (dummy_lips, dummy_mfcc)) + out_path = PRODUCTION_DIR / "coherence.pt" + torch.jit.save(traced, str(out_path)) + log.info(f" Saved TorchScript to {out_path}") + + return { + "val_auc": float(ckpt.get("val_auc", 0.0)), + "checkpoint_epoch": int(ckpt.get("epoch", -1)), + "export_format": "torchscript_trace", + } + + +def export_sstgnn(ckpt_path: Path) -> None: + """ + Export SSTGNN as state dict ONLY. + TorchScript (trace or script) is not compatible with PyG MessagePassing. + Load in detector via: + model = SSTGNNModel() + state = torch.load(path, map_location="cpu") + model.load_state_dict(state["state_dict"]) + """ + from training.phase3_sstgnn.model import SSTGNNModel + + log.info("Exporting SSTGNN model (state dict — no TorchScript) ...") + model = SSTGNNModel() + ckpt = torch.load(ckpt_path, map_location="cpu") + model.load_state_dict(ckpt["model_state"]) + model.eval() + log.info(" Model loaded OK") + + out_path = PRODUCTION_DIR / "sstgnn.pt" + torch.save({ + "state_dict": model.state_dict(), + "model_config": model.config, + "val_auc": ckpt.get("val_auc", 0.0), + "epoch": ckpt.get("epoch", -1), + }, out_path) + log.info(f" Saved state dict to {out_path}") + log.info(" NOTE: Load in src/engines/sstgnn/detector.py via model.load_state_dict()") + + return { + "val_auc": float(ckpt.get("val_auc", 0.0)), + "checkpoint_epoch": int(ckpt.get("epoch", -1)), + "export_format": "state_dict_only", + "torchscript_note": "Not supported for PyG MessagePassing models", + } + + +def main(args: argparse.Namespace) -> None: + PRODUCTION_DIR.mkdir(parents=True, exist_ok=True) + + model_card = { + "export_date": str(date.today()), + "models": {}, + } + + if args.fp_ckpt and Path(args.fp_ckpt).exists(): + meta = export_fingerprint(Path(args.fp_ckpt)) + model_card["models"]["fingerprint"] = { + "architecture": "ViT-B/16 (timm vit_base_patch16_224)", + "num_classes": 8, + "production_pt": "models/production/fingerprint.pt", + **meta, + } + + if args.co_ckpt and Path(args.co_ckpt).exists(): + meta = export_coherence(Path(args.co_ckpt)) + model_card["models"]["coherence"] = { + "architecture": "ResNet-18 visual + 1D-CNN audio + Transformer", + "production_pt": "models/production/coherence.pt", + **meta, + } + + if args.sg_ckpt and Path(args.sg_ckpt).exists(): + meta = export_sstgnn(Path(args.sg_ckpt)) + model_card["models"]["sstgnn"] = { + "architecture": "GAT 3-layer (torch_geometric GATConv)", + "production_pt": "models/production/sstgnn.pt", + **meta, + } + + card_path = PRODUCTION_DIR / "model_card.json" + with open(card_path, "w") as f: + json.dump(model_card, f, indent=2) + + log.info(f"\nModel card saved to {card_path}") + log.info("Export complete.") + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Export models for production") + p.add_argument("--fp_ckpt", default=None, help="Fingerprint best.pt") + p.add_argument("--co_ckpt", default=None, help="Coherence best.pt") + p.add_argument("--sg_ckpt", default=None, help="SSTGNN best.pt") + return p.parse_args() + + +if __name__ == "__main__": + main(parse_args()) diff --git a/training/phase6_export/integration_test.py b/training/phase6_export/integration_test.py new file mode 100644 index 0000000000000000000000000000000000000000..0897995f0e42c07a5160e390dd39c0031b372f8c --- /dev/null +++ b/training/phase6_export/integration_test.py @@ -0,0 +1,184 @@ +""" +training/phase6_export/integration_test.py + +End-to-end integration test. Final gate before calling training done. +Must pass before submitting or demoing. + +Run: + python training/phase6_export/integration_test.py +""" +from __future__ import annotations + +import logging +import sys +import tempfile +from pathlib import Path + +import numpy as np +from PIL import Image + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s") +log = logging.getLogger(__name__) + +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +PASS = " PASS" +FAIL = " FAIL" + + +def make_fake_image_bytes() -> bytes: + """Generate a synthetic 224×224 RGB image as JPEG bytes.""" + arr = (np.random.rand(224, 224, 3) * 255).astype(np.uint8) + img = Image.fromarray(arr) + import io + buf = io.BytesIO() + img.save(buf, format="JPEG") + return buf.getvalue() + + +def test_types_import(): + log.info("Testing types import ...") + from src.types import ( + GeneratorLabel, EngineResult, DetectionResponse, + GENERATOR_INDEX_TO_LABEL + ) + assert len(GeneratorLabel) == 8, f"Expected 8 generator labels, got {len(GeneratorLabel)}" + assert len(GENERATOR_INDEX_TO_LABEL) == 8 + for i in range(8): + assert i in GENERATOR_INDEX_TO_LABEL, f"Missing index {i}" + log.info(f"{PASS} types import — 8 generator labels confirmed") + + +def test_fingerprint_detector(): + log.info("Testing FingerprintDetector ...") + from src.engines.fingerprint.detector import FingerprintDetector + det = FingerprintDetector() + image = make_fake_image_bytes() + result = det.detect_bytes(image) + + assert result.engine == "fingerprint" + assert result.verdict in ("FAKE", "REAL") + assert 0.0 <= result.confidence <= 1.0 + assert result.processing_time_ms > 0 + assert isinstance(result.explanation, str) and len(result.explanation) > 0 + log.info(f"{PASS} FingerprintDetector — verdict={result.verdict} confidence={result.confidence:.4f}") + + +def test_coherence_stub(): + log.info("Testing CoherenceDetector.image_stub() ...") + from src.engines.coherence.detector import CoherenceDetector + stub = CoherenceDetector.image_stub() + assert stub.engine == "coherence" + assert stub.verdict in ("FAKE", "REAL") + log.info(f"{PASS} CoherenceDetector stub OK") + + +def test_sstgnn_stub(): + log.info("Testing SSTGNNDetector.image_stub() ...") + from src.engines.sstgnn.detector import SSTGNNDetector + stub = SSTGNNDetector.image_stub() + assert stub.engine == "sstgnn" + assert stub.verdict in ("FAKE", "REAL") + log.info(f"{PASS} SSTGNNDetector stub OK") + + +def test_fuser(): + log.info("Testing Fuser ...") + from src.types import EngineResult + from src.fusion.fuser import Fuser + + results = [ + EngineResult(engine="fingerprint", verdict="FAKE", confidence=0.92, + explanation="Test", processing_time_ms=100.0), + EngineResult(engine="coherence", verdict="REAL", confidence=0.5, + explanation="N/A — coherence analysis requires video input.", + processing_time_ms=0.0), + EngineResult(engine="sstgnn", verdict="REAL", confidence=0.5, + explanation="N/A — SSTGNN requires video input.", + processing_time_ms=0.0), + ] + + fuser = Fuser() + response = fuser.fuse(results, media_type="image", total_ms=150.0) + + assert response.verdict in ("FAKE", "REAL") + assert 0.0 <= response.confidence <= 1.0 + assert response.processing_time_ms >= 0 + assert len(response.engine_breakdown) == 3 + log.info(f"{PASS} Fuser — verdict={response.verdict} confidence={response.confidence:.4f}") + + +def test_api_import(): + log.info("Testing API import ...") + from src.api.main import app + assert app is not None + log.info(f"{PASS} API import OK") + + +def test_api_health(): + log.info("Testing API /health endpoint ...") + from fastapi.testclient import TestClient + from src.api.main import app + client = TestClient(app) + response = client.get("/health") + assert response.status_code == 200 + data = response.json() + assert data["status"] == "ok" + assert "engines" in data + log.info(f"{PASS} /health → {data}") + + +def test_api_detect_image(): + log.info("Testing API POST /detect/image ...") + from fastapi.testclient import TestClient + from src.api.main import app + client = TestClient(app) + + image_bytes = make_fake_image_bytes() + response = client.post( + "/detect/image", + files={"file": ("test.jpg", image_bytes, "image/jpeg")}, + ) + assert response.status_code == 200, f"Got {response.status_code}: {response.text}" + data = response.json() + assert data["verdict"] in ("FAKE", "REAL") + assert 0.0 <= data["confidence"] <= 1.0 + assert "engine_breakdown" in data + assert len(data["engine_breakdown"]) == 3 + log.info(f"{PASS} /detect/image → verdict={data['verdict']} conf={data['confidence']:.4f}") + + +# ── Run all tests ───────────────────────────────────────────────────────────── + +TESTS = [ + test_types_import, + test_fingerprint_detector, + test_coherence_stub, + test_sstgnn_stub, + test_fuser, + test_api_import, + test_api_health, + test_api_detect_image, +] + +if __name__ == "__main__": + passed = 0 + failed = 0 + + for test_fn in TESTS: + try: + test_fn() + passed += 1 + except Exception as e: + log.error(f"{FAIL} {test_fn.__name__}: {e}") + failed += 1 + + print(f"\n{'='*50}") + print(f"Results: {passed} passed, {failed} failed") + print(f"{'='*50}") + + if failed == 0: + print("All integration tests passed. Pipeline is ready for demo.") + else: + print("Some tests failed. Fix before demoing.") + sys.exit(1) diff --git a/training/verify_downloads.py b/training/verify_downloads.py new file mode 100644 index 0000000000000000000000000000000000000000..af72b16495b2d85c8e8dd3893a19c74bc58a3bce --- /dev/null +++ b/training/verify_downloads.py @@ -0,0 +1,78 @@ +""" +training/verify_downloads.py + +Verifies all training datasets are present and reports file counts and sizes. + +Usage: + python training/verify_downloads.py + python training/verify_downloads.py --kaggle # checks /kaggle/input/ paths +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +DATASETS_LOCAL = { + "ff++": "data/raw/ff++", + "140k_faces": "data/raw/140k_faces", + "dfdc": "data/raw/dfdc", + "celebdf": "data/raw/celebdf", + "deepfake_faces": "data/raw/deepfake_faces", + "deepfake_real": "data/raw/deepfake_real_images", + "ai_vs_real": "data/raw/ai_vs_real", +} + +DATASETS_KAGGLE = { + "140k_faces": "/kaggle/input/140k-real-and-fake-faces", + "ai_vs_real": "/kaggle/input/ai-generated-vs-real-images-datasaet", + "ff++": "/kaggle/input/faceforensics-in-compressed-videos", + "celebdf": "/kaggle/input/celeb-df", + "dfdc": "/kaggle/input/deepfake-detection-challenge", + "deepfake_faces": "/kaggle/input/deepfake-faces", + "deepfake_real": "/kaggle/input/deepfake-and-real-images", +} + +MEDIA_EXTS = {".jpg", ".jpeg", ".png", ".mp4", ".avi", ".mov"} + + +def check_dataset(name: str, path: str) -> bool: + p = Path(path) + if not p.exists(): + print(f" MISSING {name:20s} → {path}") + return False + + all_files = list(p.rglob("*")) + media_files = [f for f in all_files if f.is_file() and f.suffix.lower() in MEDIA_EXTS] + size_gb = sum(f.stat().st_size for f in all_files if f.is_file()) / 1e9 + print(f" OK {name:20s} → {len(media_files):>7,} media files {size_gb:>6.2f} GB {path}") + return True + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--kaggle", action="store_true", help="Check Kaggle input paths") + args = p.parse_args() + + datasets = DATASETS_KAGGLE if args.kaggle else DATASETS_LOCAL + label = "Kaggle" if args.kaggle else "local" + + print(f"\n=== Dataset verification ({label}) ===\n") + all_ok = True + for name, path in datasets.items(): + ok = check_dataset(name, path) + all_ok = all_ok and ok + + print() + if all_ok: + print("All datasets present. Ready to train.") + else: + missing = [n for n, p in datasets.items() if not Path(p).exists()] + print(f"Missing: {', '.join(missing)}") + if not args.kaggle: + print("Run: bash training/download_datasets.sh") + sys.exit(1) + + +if __name__ == "__main__": + main()