README_HF.md

metadata

title: Deepfake Detection Library
emoji: 🔍
colorFrom: red
colorTo: orange
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit

Deepfake Detection Library

This Space provides a unified interface to test multiple state-of-the-art deepfake detection models on your images.

Available Detectors

• R50_TF - ResNet-50 based detector trained on TrueFake dataset
• R50_nodown - ResNet-50 without downsampling operations
• CLIP-D - CLIP-based deepfake detector
• P2G - Prompt2Guard: Conditioned prompt-optimization for continual deepfake detection
• NPR - Neural Posterior Regularization

Usage

1. Upload an image
2. Select a detector from the dropdown
3. Click "Detect" to get the prediction

The detector will return:

• Prediction: Real or Fake
• Confidence: Model confidence score (0-1)
• Elapsed Time: Processing time

Models

All models have been pretrained on images generated with StyleGAN2 and StableDiffusionXL, and real images from the FFHQ Dataset and the FORLAB Dataset.

References

For more information about the implementation and benchmarking, visit the GitHub repository.

Note

⚠️ Due to file size limitations, model weights need to be downloaded automatically on first use. This may take a few moments.