---
title: Patch-MFFT Tampering Forensics API
emoji: 🛡️
colorFrom: red
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
---

# Patch-MFFT: Dual-Branch Radial Frequency Transformer API

Production FastAPI forensics service engineered for localized generative inpainting, composite blackmail splicing, and style tampering detection.

## Dual-Task Forensics Features

1. **3-Way Holistic Forensic Triage**:
   - `[0] Authentic Camera Capture`: Optical photon shot noise & Bayer demosaicing consistency
   - `[1] Holistic Generative AI`: Entire-frame deconvolution checkerboard harmonics
   - `[2] Localized Spliced / Inpainted Tampering`: Boundary phase incoherencies & local inpainting artifacts

2. **Pixel-Wise Tampering Localization**:
   - Real-time dense anomaly probability mask $\hat{\\mathbf{M}} \in [0, 1]^{384 \\times 384}$ returned as a Base64-encoded PNG heatmap.

3. **Radial Annular Fourier Decomposition**:
   - Concentric Low, Mid, and High frequency band energy distribution analysis.

## Models Served

- **Patch-MFFT Tiny**: 563K parameters (~4.2 ms GPU, 14.8 ms CPU, optimized for edge/mobile forensics)
- **Patch-MFFT Base**: 3.28M parameters (97.84% accuracy, 81.00% mIoU localization score)
- **Patch-MFFT Large**: 13.94M parameters (Maximum precision for subtle inpainting & blackmail composite detection)

## API Endpoints

- `GET /` — Healthcheck and status of loaded model variants
- `GET /models` — Specifications and parameter counts of all variants
- `POST /predict?model=large` — Multi-task forensic analysis on uploaded image

```bash
curl -X POST "https://mohsinelis-patch-mfft-api.hf.space/predict?model=large" \\
  -F "file=@sample.jpg"
```