README.md

---
title: DeepGuard AI Face Authenticator
emoji: 🛡️
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false
license: mit
tags:
  - deepfake-detection
  - computer-vision
  - tensorflow
  - mlops
short_description: Deepfake detection with 88% accuracy
---

# DeepGuard: AI Face Authenticator

A production-grade deep learning system for detecting AI-generated (deepfake) faces, built with a complete MLOps pipeline.

## Overview

DeepGuard leverages transfer learning with the Xception architecture to identify synthetic faces generated by GANs (Generative Adversarial Networks). The model achieves 88% accuracy with a 95% ROC-AUC score on StyleGAN-generated content.

## Model Performance

| Metric | Value |
|--------|-------|
| Test Accuracy | 88% |
| ROC-AUC Score | 95% |
| Training Dataset | 140,000 images |
| Architecture | XceptionTransfer |
| Input Resolution | 128x128 pixels |

## FFT Frequency Analysis Interpretation

The Fast Fourier Transform visualization provides forensic insight into image authenticity.

| Pattern | Interpretation |
|---------|----------------|
| Bright center spot | Normal low-frequency content (smooth areas) |
| Radiating spokes | Edge directions in the original image |
| Random noise distribution | Natural texture typical of real photographs |
| Grid or cross artifacts | Potential GAN fingerprint indicating synthetic generation |

Note: GAN artifacts in the frequency domain are subtle and serve as a supplementary forensic tool.

## Known Limitations

This model is trained on StyleGAN-generated faces. Detection accuracy may be reduced for:

- Images from diffusion models (Stable Diffusion, Midjourney, DALL-E)
- Non-face subjects or full-body photographs
- Heavily compressed or filtered images

## MLOps Pipeline

| Component | Technology |
|-----------|------------|
| Data Versioning | DVC |
| Experiment Tracking | MLflow + DagsHub |
| Model Training | TensorFlow / Keras |
| Deployment | Hugging Face Spaces |

## Repository

[GitHub: DeepGuard-MLOps-Pipeline](https://github.com/HarshTomar1234/DeepGuard-MLOps-Pipeline)

## License

MIT License