Triangle Attack

This repository contains code to reproduce results from the paper:

Triangle Attack: A Query-efficient Decision-based Adversarial Attack (ECCV 2022)

Xiaosen Wang, Zeliang Zhang, Kangheng Tong, Dihong Gong, Kun He, Zhifeng Li, Wei Liu

Requirements

python >= 3.6.5
pytorch == 1.7.x
numpy >= 1.15.4
imageio >= 2.6.1
torch_dct >= 0.1.5

Qucik Start

Prepare the data

Firstly, you should prepare your own benign images and victim models for attack. The pathes for the input images and model are set by --dataset_path and --modelpath, respectively. You could also download our sampled 200 images used in the experiments and adopt the the pretrained models in pytorch.

Runing attack

You could run TA as follows:

CUDA_VISIBLE_DEVICES=gpuid python TA.py --dataset_path images --csv label.csv

The generated adversarial examples would be stored in directory ./output_folder. We report the attack success rates under the thresholds of 0.1, 0.05 and 0.01 respectively.

Citation

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

@inproceedings{wang2022Triangle,
  author={Xiaosen Wang and Zeliang Zhang and Kangheng Tong and Dihong Gong and Kun He and Zhifeng Li and Wei Liu},
  booktitle = {European Conference on Computer Vision},
  title = {Triangle Attack: A Query-efficient Decision-based Adversarial Attack},
  year = {2022},
}

Contact

Questions and suggestions can be sent to xswanghuster@gmail.com.