@inproceedings{
bui2024erasing,
title={Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation},
author={Anh Tuan Bui and Long Tung Vuong and Khanh Doan and Trung Le and Paul Montague and Tamas Abraham and Dinh Phung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=GDz8rkfikp}
}
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution is to selectively removing target concepts from the model, but this may impact the remaining concepts. Prior approaches have tried to balance this by introducing a loss term to preserve neutral content or a regularization term to minimize changes in the model parameters, yet resolving this trade-off remains challenging. In this work, we propose to identify and preserving concepts most affected by parameter changes, termed as *adversarial concepts*. This approach ensures stable erasure with minimal impact on the other concepts. We demonstrate the effectiveness of our method using the Stable Diffusion model, showing that it outperforms state-of-the-art erasure methods in eliminating unwanted content while maintaining the integrity of other unrelated elements. Our code is available at \url{https://github.com/tuananhbui89/Erasing-Adversarial-Preservation}.
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
Anh Tuan Bui Long Tung Vuong Khanh Doan Trung Le Paul Montague Tamas Abraham Dinh Phung