| # Acknowledgements |
|
|
| This package is a repackaging of the official MMA-Diffusion implementation. |
| All core attack logic is the work of the original authors. Please cite the |
| paper if you use this package in your research. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{yang2024mmadiffusion, |
| title={{MMA-Diffusion: MultiModal Attack on Diffusion Models}}, |
| author={Yijun Yang and Ruiyuan Gao and Xiaosen Wang and Tsung-Yi Ho and Nan Xu and Qiang Xu}, |
| year={2024}, |
| booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}, |
| } |
| ``` |
|
|
| We also recommend citing the authors' defensive framework: |
|
|
| ```bibtex |
| @inproceedings{yang2024guardt2i, |
| title = {GuardT2I: Defending Text-to-Image Models from Adversarial Prompts}, |
| author = {Yijun Yang and Ruiyuan Gao and Xiao Yang and Jianyuan Zong and Qiang Xu}, |
| booktitle = {Advances in Neural Information Processing Systems ({NeurIPS})}, |
| volume = {37}, |
| year = {2024}, |
| url = {https://arxiv.org/abs/2403.01446}, |
| } |
| ``` |
|
|
| ## Upstream Projects |
|
|
| The original MMA-Diffusion codebase acknowledges the following open-source |
| projects: |
|
|
| - [photoguard](https://github.com/MadryLab/photoguard) |
| - [gcg](https://github.com/llm-attacks/llm-attacks) |
| - [diffusers](https://github.com/huggingface/diffusers) |
| - [unsafe-diffusion](https://github.com/YitingQu/unsafe-diffusion) |
|
|
