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Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning

<a href='https://arxiv.org/abs/2309.01246'><img src='https://img.shields.io/badge/Paper-arXiv-red'></a> <a href='https://huggingface.co/spaces/yhzhai/WSCL'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-demo-yellow'></a>

Our WSCL (weakly-supervised self-consistency learning) can detect and localize image manipulations, using only image-level binary labels for training.

This repo contains the MIL-FCN version of our WSCL implementation.

🚨News

03/2024: add demo script! Check here for more details, and check here for the online Gradio demo!

1. Setup

Clone this repo

git clone git@github.com:yhZhai/WSCL.git

Install packages

pip install -r requirements.txt

2. Data preparation

We provide preprocessed CASIA (v1 and v2), Columbia, and Coverage datasets here. 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.

python main.py --load configs/final.yaml

For evaluating a pre-trained checkpoint:

python main.py --load configs/final.yaml --eval --resume checkpoint-path

We provide our pre-trained checkpoint here.

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.

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

@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
• DETR