An unofficial implementation of the paper《Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective》

• Clone this repository. Assume this repositry is downloaded to ./DRA-BlackBoxATTACK/

• Install dependencies

  • cd DRA-BlackBoxATTACK
  • pip install -r requirements.txt

• please download the dataset from the following link and extract images to the path “./data/ImageNet-10/" imagenet10 | Kaggle

• first,please change project path config/baseconfig.py BaseConfig root and data_root to your path

• second run main.py

  python main.py  

• if choice wholeRunner, it contains a comparison with PGD

• you can change config in config/config.py

• 原始模型resnet18 （Acc: 0.99)
• 经过DRA_loss 微调过的 resnet18_DRA (Acc: 0.9564)

因为属于无目标攻击所以TOP 5下降并不多。

If you find our work and this repository useful. Please consider giving a star ⭐ and citation.

@article{zhu2022boosting,
  title={Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective},
  author={Yao Zhu, Yuefeng Chen, Xiaodan Li, Kejiang Chen, Yuan He, Xiang Tian, Bolun Zheng, Yaowu Chen, Qingming Huang},
  booktitle={IEEE Transaction on Image Processing},
  year={2022}
}