--- title: Authrix Deepfake Detector emoji: 🔍 colorFrom: green colorTo: green sdk: docker pinned: false app_port: 7860 license: mit ---

# 🔍 AUTHRIX ### AI-Powered Deepfake Detection Engine [![Python](https://img.shields.io/badge/Python-3.11+-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://python.org) [![FastAPI](https://img.shields.io/badge/FastAPI-0.111-009688?style=for-the-badge&logo=fastapi&logoColor=white)](https://fastapi.tiangolo.com) [![React](https://img.shields.io/badge/React-19-61DAFB?style=for-the-badge&logo=react&logoColor=black)](https://react.dev) [![HuggingFace](https://img.shields.io/badge/🤗_HuggingFace-ViT_Ensemble-FFD21E?style=for-the-badge)](https://huggingface.co) [![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?style=for-the-badge&logo=docker&logoColor=white)](https://docker.com) [![License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)](LICENSE) **Authrix** is a full-stack, multi-agent deepfake detection platform that analyzes videos for AI-generated content using a Vision Transformer (ViT) ensemble, temporal consistency analysis, C2PA metadata scanning, and AI audio detection — all wrapped in a sleek cyberpunk-themed dashboard and a Chrome extension. [🚀 Live Demo](https://aarav13-authrix.hf.space) · [🧩 Chrome Extension](#browser-extension) · [📡 API Reference](#api-reference) · [💬 Pricing](#pricing--tiers)

--- ## ✨ Key Features | Feature | Description | |---|---| | 🧠 **ViT Ensemble** | 2-model Vision Transformer ensemble (dima806 + prithivMLmods) with float16 batched inference | | 🎞️ **Temporal Analysis** | Detects AI video patterns: unnatural motion smoothness, temporal flickering, color drift | | 🔏 **C2PA / Metadata Scan** | Identifies AI generator signatures from Veo3, Sora, Runway, Firefly, Kling, etc. | | 🔊 **Audio Detection** | Spectral analysis for AI voice synthesis & audio-visual mismatch detection | | 🌐 **Browser Extension** | Chrome/Edge extension (MV3) that captures tab video stream for real-time analysis | | 🔗 **URL Analysis** | Paste any YouTube/TikTok/Twitter/Instagram URL — powered by yt-dlp | | 🔑 **API Key System** | Tiered access control with per-month usage quotas and Stripe billing integration | | 🐳 **Docker + Render** | One-command deployment to Render (or any Docker host / HuggingFace Spaces) | --- ## 🏗️ Architecture ``` ┌──────────────────────────────────────────────────────────────┐ │ CLIENT LAYER │ │ ┌─────────────────┐ ┌──────────────────┐ ┌────────────┐ │ │ │ React Frontend │ │ Chrome Extension │ │ REST API │ │ │ │ (Vite + TW4) │ │ (MV3, JS) │ │ Consumers │ │ │ └────────┬────────┘ └────────┬─────────┘ └─────┬──────┘ │ └───────────┼─────────────────── ┼─────────────────── ┼────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────────────────────────────────────────────────────┐ │ FastAPI BACKEND │ │ │ │ POST /analyze POST /analyze-url GET /health │ │ │ │ ┌────────────────────────────────────────────────────────┐ │ │ │ DETECTION PIPELINE │ │ │ │ │ │ │ │ Agent 0a: Metadata Agent (C2PA / AI tool scan) │ │ │ │ Agent 0b: Temporal Agent (flicker / motion CV) │ │ │ │ Agent 1: Frame Extractor (dedup, 40-frame sample) │ │ │ │ Agent 2: Face Detector (MediaPipe, single ctx) │ │ │ │ Agent 3: Decision Agent (ViT ensemble, float16) │ │ │ │ Agent 4: Report Agent (calibrated + audio fused) │ │ │ │ Agent 5: Audio Agent (librosa spectral + AV sync)│ │ │ └────────────────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────────────┘ ``` ### Detection Pipeline 1. **Metadata Agent** — Binary-scans the first 512 KB + last 64 KB of the video file for C2PA markers, XMP tags, and known AI-generator signatures (Veo, Sora, Runway, Kling, Firefly…). If a C2PA block is found, the file is immediately flagged with 98% confidence. 2. **Temporal Agent** — Measures pixel-level temporal variance, frame-difference coefficient of variation, high-frequency noise consistency, and color-channel drift across frames. Catches modern AI video generators that produce unnaturally smooth motion. 3. **Frame Extractor** — Intelligently samples up to 40 deduplicated frames, skipping near-identical consecutive frames to save inference time. 4. **Face Detector** — MediaPipe face detection runs in a **single context** across all frames (avoids repeated model init) and crops each face with 20% padding. 5. **Decision Agent (ViT Ensemble)** — All face crops are sent to **both ViT models in a single batched forward pass** (float16). Model 2 is early-exited if Model 1 is already very confident (>88% or <12%). Scores are ensemble-weighted 55/45. 6. **Audio Agent** — Extracts audio track via MoviePy/ffmpeg, runs librosa spectral analysis to detect AI voice synthesis, unnatural pitch/tempo regularity, and audio-visual sync mismatches. 7. **Report Agent** — Fuses all signals with an adaptive threshold. A C2PA hard match always wins; audio-visual mismatch overrides visual; otherwise, temporal + visual ensemble determines the final verdict with calibrated confidence. --- ## 🗂️ Project Structure ``` authrix/ ├── backend/ # FastAPI backend │ ├── main.py # App entry point, routes, middleware │ ├── detector.py # Core multi-agent detection engine (all 5 agents) │ ├── audio_detector.py # Audio analysis agent (librosa + AV sync) │ ├── auth.py # API key validation, tier limits │ ├── stripe_integration.py # Stripe billing hooks │ ├── create_owner_key.py # CLI helper to mint API keys │ ├── test_temporal.py # Unit tests for temporal analysis │ ├── requirements.txt # Python dependencies │ └── uploads/ # Temp upload directory (auto-cleaned) │ ├── frontend/ # React 19 + Vite 8 + Tailwind 4 dashboard │ ├── src/ │ │ ├── components/ # UI components (Loader, ResultCard, etc.) │ │ └── main.jsx # App entry │ ├── index.html │ ├── package.json │ └── vite.config.js │ ├── frontend-vanilla/ # Vanilla HTML/JS fallback frontend │ ├── index.html │ ├── pricing.html │ └── script.js # ~15KB — full upload + results UI │ ├── extension/ # Chrome Extension (Manifest V3) │ ├── manifest.json # Permissions, MV3 config │ ├── background.js # Service worker (tab capture) │ ├── content.js # Content script (overlay injection) │ ├── offscreen.js # Offscreen document for MediaRecorder │ ├── popup.html / popup.js # Extension popup UI │ ├── overlay.css # Injected overlay styles │ └── icons/ # Extension icons (16/48/128px) │ ├── Dockerfile # Multi-stage Docker build ├── render.yaml # Render.com deployment config ├── setup.sh / setup.bat # One-command environment setup ├── start.sh / start.bat # Dev server launcher └── BUSINESS_MODEL.md # Monetization guide & pricing ``` --- ## 🚀 Getting Started ### Prerequisites | Tool | Version | Purpose | |---|---|---| | Python | 3.11+ | Backend runtime | | Node.js | 18+ | Frontend build | | npm / pnpm | Latest | JS package manager | | ffmpeg | Any | Video conversion (auto-bundled via imageio-ffmpeg) | | Docker | 24+ | Containerized deployment (optional) | > **Windows users:** ffmpeg is bundled via `imageio-ffmpeg` — no manual install required. --- ### Option A — Quick Start (Local Dev) #### 1. Clone the Repository ```bash git clone https://github.com/Aarav-bit/Authrix.git cd Authrix ``` #### 2. Backend Setup ```bash cd backend python -m venv ../venv # Activate (Linux/macOS) source ../venv/bin/activate # Activate (Windows) ..\venv\Scripts\activate pip install -r requirements.txt ``` > **Note:** First startup downloads ~2 GB of ViT model weights from HuggingFace. Subsequent starts use the local cache. #### 3. Start the Backend ```bash uvicorn main:app --host 0.0.0.0 --port 8000 --reload ``` The API will be live at **http://localhost:8000** and the vanilla frontend will be served automatically. #### 4. (Optional) Start the React Frontend ```bash cd ../frontend npm install npm run dev ``` React dashboard available at **http://localhost:5173**. --- ### Option B — One-Command Setup Scripts ```bash # Linux / macOS ./setup.sh ./start.sh # Windows setup.bat start.bat ``` --- ### Option C — Docker ```bash # Build image docker build -t authrix . # Run docker run -p 7860:7860 authrix ``` Open **http://localhost:7860**. --- ## 🌐 Deployment ### Render (Recommended) 1. Fork this repository. 2. Create a new **Web Service** on [Render](https://render.com). 3. Connect your GitHub repo — Render auto-detects `render.yaml`. 4. Set env vars (see below). 5. Deploy. ✅ `render.yaml` configures: - Runtime: Docker - Health check: `GET /health` - Port: `8000` ### HuggingFace Spaces The `Dockerfile` is pre-configured for HuggingFace Spaces (port 7860, user 1000) and pre-caches both ViT models at build time. 1. Create a new Space → **Docker** runtime. 2. Push this repo as the Space source. 3. Models are cached in the image — cold start is instant. ### Manual VPS ```bash # Pull latest git pull origin main # Build frontend cd frontend && npm run build cp -r dist ../frontend-dist # Install Python deps cd ../backend pip install -r requirements.txt # Start with Gunicorn (production) uvicorn main:app --host 0.0.0.0 --port 8000 --workers 2 ``` --- ## ⚙️ Environment Variables | Variable | Required | Description | Example | |---|---|---|---| | `PORT` | No | Port to bind | `8000` | | `PYTHONUNBUFFERED` | No | Force stdout flush | `1` | | `STRIPE_SECRET_KEY` | Optional | Stripe billing | `sk_live_...` | | `STRIPE_WEBHOOK_SECRET` | Optional | Stripe webhooks | `whsec_...` | > API keys for end-users are stored in `backend/api_keys.json` (auto-generated). No external database required. --- ## 🔑 API Reference ### Base URL ``` https://aarav13-authrix.hf.space (production) http://localhost:8000 (local) ``` ### Authentication Pass your API key as a header. For local development, the key is optional. ``` X-API-Key: authrix_YOUR_KEY_HERE ``` --- ### `GET /health` Check server readiness. ```bash curl https://aarav13-authrix.hf.space/health ``` **Response:** ```json { "status": "ok", "model": "Ensemble (2 ViT models)", "ready": true } ``` --- ### `POST /analyze` Analyze an uploaded video file for deepfake content. ```bash curl -X POST http://localhost:8000/analyze \ -H "X-API-Key: authrix_YOUR_KEY" \ -F "file=@/path/to/video.mp4" ``` **Supported formats:** `.mp4`, `.avi`, `.mov`, `.mkv`, `.webm`, `.wmv` **Max file size:** 100 MB **Response:** ```json { "result": "FAKE", "confidence": 87.3, "details": { "visual_score": 0.82, "audio_result": "AI_VOICE", "temporal_signals": ["Perfectly uniform motion (CV=0.01)"], "metadata_signals": ["c2pa", "tool:runway"], "face_coverage": 0.92, "frames_analyzed": 38 }, "frame_timeline": [ { "frame_index": 0, "fake_probability": 0.84 }, { "frame_index": 5, "fake_probability": 0.79 } ], "metadata": { "frames_analyzed": 38, "frames_with_faces": 35, "video_duration_sec": 12.4, "video_fps": 30.0, "resolution": "1280x720" } } ``` --- ### `POST /analyze-url` Analyze a video from a URL (YouTube, TikTok, Twitter, Instagram, etc.). ```bash curl -X POST http://localhost:8000/analyze-url \ -H "Content-Type: application/json" \ -d '{"url": "https://www.youtube.com/watch?v=..."}' ``` **Response:** Same structure as `/analyze`. --- ### Error Codes | Status | Meaning | |---|---| | `400` | Bad request (unsupported format, invalid URL) | | `401` | Invalid or missing API key | | `413` | File too large (>100 MB) | | `429` | Monthly usage limit exceeded | | `503` | Server still initializing — retry in 30s | --- ## 💰 Pricing & Tiers | Tier | Price | Analyses / Month | Features | |---|---|---|---| | **Free** | $0 | 10 | Extension, 2-min videos, community support | | **Pro** | $9.99/mo | 100 | 10-min videos, API access (100 calls), email support | | **Business** | $49/mo | 1,000 | Unlimited length, API (5K calls), white-label reports | | **Enterprise** | Custom | Unlimited | On-premise, custom training, SLA, dedicated support | ### Pay-Per-Use API | Video Length | Price | |---|---| | < 5 min | $0.05 | | 5–15 min | $0.10 | | > 15 min | $0.25 | ### Generate an API Key (Self-Hosted) ```bash cd backend python create_owner_key.py # Or: python -c "from auth import create_api_key; print(create_api_key('you@email.com', 'pro'))" ``` --- ## 🧩 Browser Extension The Authrix Chrome Extension (v2.2.0, Manifest V3) allows one-click deepfake analysis of any video playing in your browser tab. ### How It Works 1. User clicks the Authrix toolbar icon while a video is playing. 2. The background service worker uses the `tabCapture` API to start recording the tab's media stream. 3. An offscreen document captures ~8 seconds of video via `MediaRecorder`. 4. The clip is posted to the Authrix API and the result is overlaid on the page. ### Install (Developer Mode) 1. Open `chrome://extensions` 2. Enable **Developer Mode** (top-right toggle) 3. Click **Load unpacked** → select the `extension/` folder 4. The Authrix icon appears in your toolbar ### Permissions | Permission | Reason | |---|---| | `tabCapture` | Record tab video stream | | `scripting` | Inject result overlay | | `storage` | Cache API key & usage | | `offscreen` | Run MediaRecorder out-of-context | | `contextMenus` | Right-click menu | --- ## 🛠️ Development ### Backend ```bash # Run with hot-reload uvicorn main:app --reload --port 8000 # Run tests cd backend python test_temporal.py # Lint flake8 . --max-line-length=120 ``` ### Frontend (React) ```bash cd frontend npm run dev # Dev server with HMR npm run build # Production build → dist/ npm run lint # ESLint npm run preview # Preview production build ``` ### Available Scripts Summary | Command | Description | |---|---| | `uvicorn main:app --reload` | Backend dev server | | `npm run dev` | React frontend dev server | | `npm run build` | Build React app for production | | `python create_owner_key.py` | Generate a new API key | | `docker build -t authrix .` | Build Docker image | --- ## 🔬 Tech Stack ### Backend - **FastAPI 0.111** — Async REST API with automatic OpenAPI docs - **Python 3.11** — Core runtime - **OpenCV 4.9** — Video decoding and frame extraction - **MediaPipe 0.10** — Face detection (single-context optimized) - **HuggingFace Transformers** — ViT model loading and inference - **PyTorch 2.3+** — Float16 batched tensor inference - **librosa 0.10** — Audio feature extraction and spectral analysis - **imageio-ffmpeg** — Bundled ffmpeg binary for video conversion - **yt-dlp** — URL-based video download (YouTube, TikTok, etc.) - **Stripe** — Payment processing and subscription management ### Frontend - **React 19** — UI library - **Vite 8** — Build tool and dev server - **Tailwind CSS 4** — Utility-first styling - **Three.js + @react-three/fiber** — 3D particle effects - **Framer Motion** — Animations - **Zustand** — Lightweight state management ### Infrastructure - **Docker** — Containerization - **Render** — PaaS deployment - **HuggingFace Spaces** — Model hosting and demo deployment ### AI Models | Model | Source | Purpose | |---|---|---| | `dima806/deepfake_vs_real_image_detection` | HuggingFace | Primary ViT classifier | | `prithivMLmods/Deep-Fake-Detector-v2-Model` | HuggingFace | Secondary ViT classifier | --- ## 🔧 Troubleshooting ### Server takes a long time to start **Cause:** HuggingFace models (~1–2 GB) are being downloaded on first run. **Fix:** Wait ~2–5 minutes. Subsequent starts use the local cache at `~/.cache/huggingface/`. ### `Could not open video` / OpenCV error on Windows **Cause:** OpenCV on Windows cannot natively decode `.webm` or `.mkv`. **Fix:** The backend automatically converts these via bundled ffmpeg. Ensure `imageio-ffmpeg` is installed: ```bash pip install imageio-ffmpeg ``` ### Extension not sending data to the API **Cause:** The extension is hard-coded to connect to `http://localhost:8000` (dev) or `https://aarav13-authrix.hf.space` (prod). **Fix:** Update `host_permissions` in `extension/manifest.json` to match your deployment URL, then reload the extension. ### `429 Monthly limit exceeded` **Cause:** Your API key has hit its monthly quota. **Fix:** Upgrade your plan, or generate a new owner key locally: ```bash python create_owner_key.py ``` ### `503 Server still initializing` **Cause:** The ViT models haven't finished loading yet. **Fix:** Hit `GET /health` and wait until `"ready": true`, then retry. ### Audio analysis not available **Cause:** `librosa`, `soundfile`, or `moviepy` not installed, or the video has no audio track. **Fix:** ```bash pip install librosa soundfile moviepy ``` --- ## 🗺️ Roadmap - [ ] Firefox extension support - [ ] Real-time video stream analysis via WebSocket - [ ] Mobile app (React Native) - [ ] Batch analysis endpoint for enterprise workflows - [ ] Webhook notifications for async analysis - [ ] GDPR-compliant EU data residency option - [ ] On-premise deployment Helm chart - [ ] Fine-tuned model on latest Veo3 / Sora outputs --- ## 🤝 Contributing Contributions are welcome! Please follow these steps: 1. Fork the repository 2. Create a feature branch: `git checkout -b feat/my-feature` 3. Commit your changes: `git commit -m 'feat: add my feature'` 4. Push to the branch: `git push origin feat/my-feature` 5. Open a Pull Request Please make sure your code passes linting before submitting. --- ## 📄 License This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details. --- ## 📧 Contact | Channel | Link | |---|---| | Enterprise Sales | enterprise@authrix.ai | | Live Demo | https://aarav13-authrix.hf.space | | API Docs | https://aarav13-authrix.hf.space/docs | ---

**Built with ❤️ by the Authrix Team** *Fighting misinformation, one frame at a time.*