Revelio's Effects on WikiArt Dataset. The first row: Recovered images on data used during fine-tuning (membership data). The second row: Recovered images on data not used during fine-tuning (holdout data). Revelio provides visualizable identification of copyright infringements.
Revelio is a strong copyright authentication tool that provides visualizable identification of copyright infringement during fine-tuning on diffusion models. Revelio accepts a half-masked image as input and exploits the conceptual difference between pretrained and fine-tuned models. A high visual similarity can be found when the input image is used during fine-tuning while a high discrepancy can be found when it is not used during fine-tuning. For more details about the methodology, please refer to our paper. It also discusses the importance of visual identification in the context of copyright authentication.
- 2024.02: Our paper about Revelio "CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion" is accepted by CVPR 2024.
Run following code to install requirements:
pip install -r requirements.txt
Provided fine-tuning code is based on dreambooth. Run following command to fine-tune a model from Van Gogh's paintings and from sampled dog's images:
python Trainer.py
The model checkpoints will be saved in path db_prior.
Run the following code to remove and reconstruct partial information of the given input images:
python Extractor.py
>= 16GB Vram is required as we need load both the pretrained and fine-tuned models to compare them. The partial representation and the reconstructed images are saved in Recovered_Samples.
The result is visualizable. To directly quantify the visual effects, a clip or dino model can be used, which aligns to human vision measurements. Run following command to see Acc. and AUC of revelio based on clip and dino similarity:
python Validator.py
- Revelio Implementation (2024.3.18)
- Reduce VRAM costs
- Combine with non-visual aligned measurements.
This project is licensed under the Apache-2.0 license.
If you find our work valuable and utilize it, we kindly request that you cite our paper.
@article{wu2024cgi,
title={CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion},
author={Wu, Xiaoyu and Hua, Yang and Liang, Chumeng and Zhang, Jiaru and Wang, Hao and Song, Tao and Guan, Haibing},
journal={arXiv preprint arXiv:2403.11162},
year={2024}
}