Official repository for CVPR 2022 paper Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution.
This project is developed based on Python 3.6.
pip install -r requirements.txt
Download the pretrained models and dataset [download link] and unzip it with
unzip pretrained.zip
Then you can conduct the untargeted attack for CIFAR-10 evaluation without training.
- Evaluate our CG-ES against TARGET_MODEL [resnet.sh|densenet.sh|vgg.sh|pyramidnet.sh] by running
sh scripts/cifar_unt/TARGET_MODEL
Please cite our paper in your publications if it helps your research:
@inproceedings{Feng_CGATTACK_2022,
title={Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution},
author={Feng, Yan and Wu, Baoyuan and Fan, Yanbo and Liu, Li and Li, Zhifeng and Xia, Shutao},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}