Xuanyu Zhang, Runyi Li, Jiwen Yu, Youmin Xu, Weiqi Li, Jian Zhang
School of Electronic and Computer Engineering, Peking University
- 🎉🎉🎉 Our code has been released. Our project is still being updated.
- 🎉🎉🎉 Congratulations on EditGuard being accepted by CVPR 2024! Our open-source project is making progress, stay tuned for updates!
pip install -r requirements.txt
cd code
python app.py
Download the testing dataset and place it in the "./dataset/valAGE-Set" and "./dataset/valAGE-Set-Mask". Download the pre-trained checkpoint and put it in the "./checkpoints".
cd code
python test.py -opt options/test_editguard.yml --ckpt ../checkpoints/clean.pth
To extract the tampered masks:
python maskextract.py --threshold 0.2
Download the COCO2017 dataset and modify the path of the training dataset in the config file.
Stage 1: Train the BEM and BRM.
python train_bit.py -opt options/train_editguard_bit.yml
Stage 2: First modify the checkpoint path of pretrained BEM and BRM in Line 87 "pretrain_model_G: " of "train_editguard_image.yml". Then, please run:
python train.py -opt options/train_editguard_image.yml
We propose a versatile proactive forensics framework EditGuard. The application scenario is shown on the left, wherein users embed invisible watermarks to their images via EditGuard in advance. If suffering tampering, users can defend their rights via the tampered areas and copyright information provided by EditGuard. Some supported tampering methods (marked in blue) and localization results of EditGuard are placed on the right. Our EditGuard can achieve over 95% localization precision and nearly 100% copyright accuracy.
Our EditGuard can pinpoint pixel-wise tampered areas under different AIGC-based editing methods.
Our EditGuard can be easily modified and adapted to video tamper localization and copyright protection.
| Original Video | Watermarked Video | Tampered Video | Predicted Mask |
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@inproceedings{zhang2024editguard,
title={Editguard: Versatile image watermarking for tamper localization and copyright protection},
author={Zhang, Xuanyu and Li, Runyi and Yu, Jiwen and Xu, Youmin and Li, Weiqi and Zhang, Jian},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11964--11974},
year={2024}
}