This if the official implementation for paper: SneakyPrompt: Jailbreaking Text-to-image Generative Models
Our work has been reported by MIT Technology Review and JHU Hub. Please check them out if interested.
The experiment is run on Ubuntu 18.04, with one Nvidia 3090 GPU (24G). Please install the dependencies via:
conda env create -f environment.yml
The nsfw_200.txt can be access per request, please send the author an email for password.
Note: This dataset may contain explicit content, and user discretion is advised when accessing or using it.
- Do not intend to utilize this dataset for any NON-research-related purposes.
- Do not intend to distribute or publish any segments of the data.
python main.py --target='sd' --method='rl' --reward_mode='clip' --threshold=0.26 --len_subword=10 --q_limit=60 --safety='ti_sd'
You can change the parameters follow the choices in 'search.py'. The adversarial prompts and statistic results (xx.csv) will be saved under '/results', and the generated images will be saved under '/figure'
python evaluate.py --path='PATH OF xx.csv'
Please cite our paper if you find this repo useful.
@inproceedings{yang2023sneakyprompt,
title={SneakyPrompt: Jailbreaking Text-to-image Generative Models},
author={Yuchen Yang and Bo Hui and Haolin Yuan and Neil Gong and Yinzhi Cao},
year={2024},
booktitle={Proceedings of the IEEE Symposium on Security and Privacy}
}