Diversifying the High-level Features

This repository contains code implementing the idea of the paper:

Diversifying the High-level Features for better Adversarial Transferability (BMVC 2023)

Zhiyuan Wang, Zeliang Zhang, Siyuan Liang, Xiaosen Wang

We also include the torch version code in the framework TransferAttack.

Requirements

• Python >= 3.7
• Tensorflow = 1.14.0
• NumPy >= 1.21.6
• SciPy >= 1.1.0
• Pandas >= 1.0.1
• imageio >= 2.9.0

Qucik Start

Prepare the data and models

Firstly, you should prepare your own benign images and the corresponding labels. The path for the input images and labels are set by --input_dir. You can download the data here.

Next, you should prepare some pretrained models and place them in directory ./models. Some pretrained models can be downloaded here and here.

Runing attack

You could run DHF as follows:

python mi_fgsm.py --input_dir ./dev_data --method dhf --arch res_101

The generated adversarial examples would be stored in directory ./results and the attack success rates will be reported.

Citation

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

@inproceedings{wang2023diversifying,
     title={{Diversifying the High-level Features for better Adversarial Transferability}},
     author={Zhiyuan Wang and Zeliang Zhang and Siyuan Liang and Xiaosen Wang},
     booktitle={Proceedings of the British Machine Vision Conference},
     year={2023},
}