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.

Install prerequisites

pip install -r requirements.txt

Download pre-trained model

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.

Robustness evaluation

• Evaluate our CG-ES against TARGET_MODEL [resnet.sh|densenet.sh|vgg.sh|pyramidnet.sh] by running

sh scripts/cifar_unt/TARGET_MODEL

Citation

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}
}