README.md

metadata

title: DocForensics
emoji: 🔍
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 7860
pinned: false

🔍 DocForensics — Document Tampering & Forgery Detector

A computer-vision system that inspects documents and images for signs of manipulation. It combines seven classical forensic detectors with a trained two-stream CNN and fuses their signals into a single verdict — AUTHENTIC, TAMPERED, or AI-GENERATED — with a heatmap showing where the suspicion lies.

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How it works

The uploaded file is preprocessed, then analyzed by eight detectors in parallel. Their scores and heatmaps are fused into one verdict.

Detector	Looks for
ELA	Error-level inconsistencies from re-compression
Noise	Local noise-fingerprint mismatches at splice edges
Copy-Move	Cloned/duplicated regions (offset-clustered)
Double-JPEG	Periodic artifacts of re-compression
Font forensics	Baseline / anti-aliasing inconsistencies in text
Metadata	Editor software, incremental PDF edits, date mismatches
AI-generated	Frequency signature + a Hugging Face classifier
TamperNet (CNN)	Learned tamper localization — the primary signal

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The model — TamperNet

TamperNet is a two-stream U-Net. One stream sees the RGB image; the other sees a noise residual produced by fixed SRM high-pass filters, which exposes the subtle noise discontinuities that editing leaves behind. The streams are encoded separately, fused at a bottleneck, and decoded with skip connections back to a full-resolution tamper mask. A parallel classification head produces an overall tampered probability.

• Two streams — RGB (content) + SRM noise (forensic residual)
• Encoder — 3 conv blocks per stream (32 → 64 → 128 channels) with pooling
• Bottleneck — concatenates both streams and fuses them
• Two heads — a classifier (tampered probability) and a U-Net decoder (mask/heatmap)
• Skip connections preserve spatial detail so the heatmap is sharp

Training. The critical detail is the dataset: genuine and tampered classes contain the same source documents (a clean augmented copy and a forged copy of each), so the network learns tampering rather than memorizing document identity. With balanced classes (pos_weight ≈ 1.0), validation AUC reaches ~0.94.

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Fusion & verdict

Detector scores are combined with a weighted average, plus a strong-signal boost so a single confident detector can't be averaged into silence. The most reliable signals (CNN, AI-classifier, ELA) carry the most weight; copy-move and double-JPEG — which are noisy on repetitive text — are low-weight hints.

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Architecture

ingestion → preprocessing → OCR → ┌─ 7 forensic detectors ─┐
                                   ├─ TamperNet CNN          ├─ fusion → Verdict + heatmap
                                   └────────────────────────┘
                       FastAPI (REST + serves the React UI)

Tech stack — FastAPI · PyTorch · OpenCV · scikit-image · PyMuPDF · Transformers · React + Vite. 87 tests across unit / integration / API / model layers.

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Quick start (local dev)

# Backend
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
uvicorn api.main:app --port 8000

# Frontend (separate terminal)
cd frontend && npm install && npm run dev
# open http://localhost:5173   (proxies /api → :8000)

Run the whole app in one container

docker compose up --build
# open http://localhost:8000

Tests

pytest tests/ -v        # 87 tests

Deployment

See DEPLOYMENT.md — single-container Docker, with step-by-step guides for Hugging Face Spaces, Render, Railway, Fly.io, and split frontend/backend hosting.

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Honest limitations

• Trained on synthetic forgeries (CPU). It reliably separates clean documents from forged ones, but very small/subtle copy-move patches can sit near the decision boundary.
• Copy-move and double-JPEG are weak on text-heavy documents (legitimate repetition mimics tampering), so they're low-weight hints; the CNN carries the verdict.
• The first request downloads a ~350 MB AI-detector model, then caches it. Set DOCFORENSICS_DISABLE_AI_MODEL=1 to skip it on memory-constrained hosts.

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Author

Developed by Surya Karthik

• 💼 LinkedIn: linkedin.com/in/surya-karthik-
• 📧 Get in touch: g.suryakarthik@gmail.com

Feedback and contributions are welcome — feel free to open an issue or reach out.