README.md

metadata

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

License

MIT License