Official PyTorch implementation of Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks (ICIP 2022).
git clone https://github.com/wkim97/ADA.git
conda install --file requirements.txt
Download the training and evaluation datasets
here
and unzip the file under ADA/data
.
The official evaluation dataset can also be downloaded from the NIPS 2017 adversarial attack competition.
You can download the pretrained weights
here
and unzip the file under ADA/weights
.
python train.py --surrogate inception_v3 --target_layer Mixed_7c --save_dir ./weights --save_name default
python test.py --surrogate inception_v3 --target_layer Mixed_7c --load_dir ./weights --load_name default
Some parts of the code are borrowed from grad-cam-pytorch and from DSGAN.
If you find this code useful for your research, please consider citing our paper
@article{kim2022diverse,
title={Diverse Generative Adversarial Perturbations on Attention Space for Transferable Adversarial Attacks},
author={Kim, Woo Jae and Hong, Seunghoon and Yoon, Sung-Eui},
journal={arXiv preprint arXiv:2208.05650},
year={2022}
}