How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
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:
The main features of crywang/rfm are: Detection Models.
Open-source alternatives to crywang/rfm include: cfeng16/audio-visual-forensics — Self-Supervised Video Forensics by Audio-Visual Anomaly Detection Chao Feng, Ziyang Chen, Andrew Owens University of… chuangchuangtan/lgrad — Beijing Jiaotong University, YanShan University. chuangchuangtan/npr-deepfakedetection — Beijing Jiaotong University, YanShan University, A*Star. frederickszk/lrnet — Landmark Recurrent Network: An efficient and robust framwork for Deepfakes detection. grip-unina/id-reveal — Davide Cozzolino, Andreas Rössler, Justus Thies, Matthias Nießner, Luisa Verdoliva. 10ring/laa-net — This is an official implementation for LAA-Net! [Paper].
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection Chao Feng, Ziyang Chen, Andrew Owens University of Michigan, Ann Arbor
Beijing Jiaotong University, YanShan University, A*Star
