Awesome
Representative Forgery Mining for Fake Face Detection
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}
}
Overview
Setup
This repository is build upon Python v3.8 and Pytorch v1.7.0 on Ubuntu 18.04.
You have to request datasets from:
-
FaceForensics ++ : Learning to Detect Manipulated Facial Images
-
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
and extra model from:
For training:
The default baseline model is xception. If the datasets mentioned above are ready to use, run:
python train.py
For visualization:
Average FAM can be generated for representative forgery visualization, run:
python AvgFAM.py
Contact:
If you have any questions about our work, feel free to contact us through email (Chengrui Wang: crwang@bupt.edu.cn).