| # Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning | |
| > [**Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning**](https://arxiv.org/abs/2309.01246) | |
| > | |
| > [Yuanhao Zhai](https://www.yhzhai.com), [Tianyu Luan](https://tyluann.github.io), [David Doermann](https://cse.buffalo.edu/~doermann/), [Junsong Yuan](https://cse.buffalo.edu/~jsyuan/) | |
| > | |
| > University at Buffalo | |
| > | |
| > ICCV 2023 | |
| This repo contains the MIL-FCN version of our WSCL implementation. | |
| ## 1. Setup | |
| Clone this repo | |
| ```bash | |
| git clone git@github.com:yhZhai/WSCL.git | |
| ``` | |
| Install packages | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## 2. Data preparation | |
| We provide preprocessed CASIA (v1 and v2), Columbia, and Coverage datasets [here](https://buffalo.box.com/s/2t3eqvwp7ua2ircpdx12sfq04sne4x50). | |
| Place them under the `data` folder. | |
| ## 3. Training and evaluation | |
| Runing the following script to train on CASIAv2, and evalute on CASIAv1, Columbia and Coverage. | |
| ```shell | |
| python main.py --load configs/final.yaml | |
| ``` | |
| ## Citation | |
| If you feel this project is helpful, please consider citing our paper | |
| ```bibtex | |
| @inproceedings{zhai2023towards, | |
| title={Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning}, | |
| author={Zhai, Yuanhao and Luan, Tianyu and Doermann, David and Yuan, Junsong}, | |
| booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, | |
| pages={22390--22400}, | |
| year={2023} | |
| } | |
| ``` | |
| ## Acknowledgement | |
| We would like to thank the following repos for their great work: | |
| - [awesome-semantic-segmentation-pytorch](https://github.com/Tramac/awesome-semantic-segmentation-pytorch) | |
| - [DETR](https://github.com/facebookresearch/detr) | |