A Unified Approach to Interpreting and Boosting Adversarial Transferability

Here are codes for our paper A Unified Approach to Interpreting and Boosting Adversarial Transferability(ICLR 2021).

Xin Wang*, Jie Ren*, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang

- Requirements

• python 3.8.3
• pytorch 1.6.0
• torchvision 0.7.0
• pretrained models 0.7.4

- How to Use

Get Dataset:

We randomly select validation images from the ImageNet dataset, which can be correctly classified by all source models. You can download the images at Google Drive, then place the folder images_1000 to ./data/.

Test the transferability of IR Attack:

We run the code to generate adversarial examples with source arch ResNet34 and test its transferability on 7 targets we used in our paper, using the following commands.

• PGD

python main_interaction_loss.py --arch resnet34 --att PGD --p inf

• PGD+IR

python main_interaction_loss.py --arch resnet34 --lam 1 --att IR  --p inf

• SGM+IR

python main_interaction_loss.py --arch resnet34 --lam 1  --gamma 0.2 --att SGM+IR  --p inf

- Reproduced results

The results are shown as follows.

Method \ Target	VGG-16	RN-152	DN-201	SE-154	IncV3	IncV4	IncResV2
PGD L_inf	0.668	0.563	0.635	0.326	0.253	0.202	0.221
PGD L_inf +IR	0.857	0.858	0.864	0.635	0.539	0.538	0.498
SGM + IR	0.940	0.922	0.931	0.704	0.696	0.663	0.626

• Update 4/22/2022: we notice that the results may change with different versions of pytorch. The results run with the same command but different versions of pytorch are shown as follows. Thus, if you want to reproduce similar reusults in the paper, please use pytorch=1.6.0 and torchvision=0.7.0.

python main_interaction_loss.py --arch resnet34 --lam 1 --att IR  --p inf

Method \ Target	VGG-16	RN-152	DN-201	SE-154	IncV3	IncV4	IncResV2
PGD L_inf +IR (pytorch=1.6.0)	0.857	0.858	0.864	0.635	0.539	0.538	0.498
PGD L_inf +IR (pytorch=1.10.0)	0.785	0.768	0.799	0.622	0.545	0.519	0.476

- Citation

Please cite the following paper, if you use this code.

@inproceedings{
wang2021a,
title={A Unified Approach to Interpreting and Boosting Adversarial Transferability},
author={Xin Wang and Jie Ren and Shuyun Lin and Xiangming Zhu and Yisen Wang and Quanshi Zhang},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=X76iqnUbBjz}
}

- Contact

If you have any question, please feel free to contact us via [email protected].