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# Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
<p align="center">
<a href="https://arxiv.org/abs/2309.01246"><b>Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning</b></a>
</p>
<p align="center">
<a href="https://www.yhzhai.com">Yuanhao Zhai</a>,
<a href="https://tyluann.github.io">Tianyu Luan</a>,
<a href="https://cse.buffalo.edu/~doermann/">David Doermann</a>,
<a href="https://cse.buffalo.edu/~jsyuan/">Junsong Yuan</a>
</p>
<p align="center">
University at Buffalo
</p>
<p align="center">
ICCV 2023
</p>
This repo contains the MIL-FCN version of our WSCL implementation.
## 🚨News
**03/2024**: add demo script! Check [here](https://github.com/yhZhai/WSCL?tab=readme-ov-file#4-demo) for more details!
## 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.
For other datasets, please prepare a json datalist file with similar structure as the existing datalist files in the `data` folder. After that, adjust the `train_dataslist` or the `val_datalist` entries in the configuration files `configs/final.yaml`.
## 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
```
For evaluating a pre-trained checkpoint:
```shell
python main.py --load configs/final.yaml --eval --resume checkpoint-path
```
We provide our pre-trained checkpoint [here](https://buffalo.box.com/s/2t3eqvwp7ua2ircpdx12sfq04sne4x50).
## 4. Demo
Running our manipulation model on your custom data!
Before running, please configure your desired input and output path in the `demo.py` file.
```shell
python demo.py --load configs/final.yaml --resume checkpoint-path
```
By default, it evaluates all `.jpg` files in the `demo` folder, and saves the
detection result in `tmp`, with manipulation probablities appended to the file names.
## 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)
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