End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection

This is the official implementation of COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection

Installation

First install Detectron2 following the official guide: INSTALL.md.

Then build AdelaiDet with:

git clone https://github.com/zhangconghhh/COMICS.git
cd COMICS
python setup.py build develop

Quick Start

To train a model with COMICS, first setup the corresponding datasets following datasets/README.md, then run:

sh train_comics.sh

To evaluate the model after training, run:

sh eval_comics.sh

Cite the paper

If this work is helpful to you, please cite it as:

@ARTICLE{comics_zhanng,
  author={Zhang, Cong and Qi, Honggang and Wang, Shuhui and Li, Yuezun and Lyu, Siwei},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  title={COMICS: End-to-End Bi-Grained Contrastive Learning for Multi-Face Forgery Detection},
  year={2024},
  volume={34},
  number={10},
  pages={10223-10236},
  doi={10.1109/TCSVT.2024.3405563}}

Acknowledgements

The codes are modified from AdelaiDet. Thanks for their open source.