This repository contains the Pytorch implementation of Representative Forgery Mining for Fake Face Detection. If you find our code useful in your research, please cite:

@inproceedings{wangCVPR21rfm,
  author    = {Wang, Chengrui and Deng, Weihong},
  title     = {Representative Forgery Mining for Fake Face Detection},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2021}
}

This repository is build upon Python v3.8 and Pytorch v1.7.0 on Ubuntu 18.04.

You have to request datasets from:

1. FaceForensics ++ : Learning to Detect Manipulated Facial Images

2. On the Detection of Digital Face Manipulation

3. Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics

and extra model from:

1. What makes fake images detectable? Understanding properties that generalize

The default baseline model is xception. If the datasets mentioned above are ready to use, run:

python train.py

Average FAM can be generated for representative forgery visualization, run:

python AvgFAM.py

If you have any questions about our work, feel free to contact us through email (Chengrui Wang: [email protected]).