Structure Invariant Attack

This repository contains code to reproduce results from the paper:

Structure Invariant Transformation for better Adversarial Transferability (ICCV 2023)

Xiaosen Wang, Zeliang Zhang, Jianping Zhang

We also include the code in the framework TransferAttack.

Requirements

• Python >= 3.6.5
• Numpy >= 1.15.4
• opencv >= 3.4.2
• scipy > 1.1.0
• pandas >= 1.0.1
• imageio >= 2.6.1
• pytorch >= 1.14.0
• torchvision >= 0.13

Qucik Start

Prepare the data and models

You should download the data offered by Admix and place it in Input/data with label file val_rs.csv in Input.

Runing attack

Taking SIA attack for example, you can run this attack as following:

CUDA_VISIBLE_DEVICES=gpuid python main.py --model model_name  

Evaluating the attack

The generated adversarial examples would be stored in directory ./outputs. Then run the file main.py with eval to evaluate the success rate of each model used in the paper:

CUDA_VISIBLE_DEVICES=gpuid python main.py --eval

Citation

If you find the idea or code useful for your research, please consider citing our paper:

@inproceedings{wang2023structure,
     title={{Structure Invariant Transformation for better Adversarial Transferability}},
     author={Xiaosen Wang and Zeliang Zhang and Jianping Zhang},
     booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
     year={2023}
}