Awesome

RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation with Natural Prompts

Install

CLIP

Please follow the guidelines in CLIP Github Repository to install CLIP

DALL•E Mini

Run the following command to install DALL•E Mini:

$ pip install min-dalle

Go into the following folder:

$ cd /target_model/min_dalle/pretrained

Download and uncompress the files: dalle-bart and vqgan

Word2Vec

Go into the folder:

$ cd /Word2Vec

Download the files: word2id.pkl and wordvec.pkl

Run attack

To run our attack:

python run_attack.py --ori_sent [original sentence] --tar_img_path [target image path] --tar_sent [target sentence] --log_save_path [log save path] --intem_img_path [intermediate results save path] --best_img_path [output best images save path] --mutate_by_impor [whether select the word by importance in mutation]

For a quick demo:

python run_attack.py --ori_sent "a herd of cows that are grazing on the grass" --tar_img_path "./target.png" --tar_sent "a large red and white boat floating on top of a lake"

Citation

If you find our work useful, please cite:

@InProceedings{Liu_2023_CVPR,
    author    = {Liu, Han and Wu, Yuhao and Zhai, Shixuan and Yuan, Bo and Zhang, Ning},
    title     = {RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {20585-20594}
}

Acknowledements

Thanks for the open souce code:

CLIP: https://github.com/openai/CLIP

DALL•E Mini： https://github.com/kuprel/min-dalle

OpenAttack: https://github.com/thunlp/OpenAttack