This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack".
PyTorch >= 1.6.0
TensorBoard >= 2.2.1
tqdm
Download the data and CE trained model of VGGFace from:
https://github.com/tongwu2020/phattacks/releases/tag/Data%26Model
Download the data of ImageNet from:
- Dynamic patch attack with GDPA:
python gdpa.py --dataset [imagenet|vggface] --data_path [FOLDER_NAME]
If on VGGFace, please add --vgg_model_path [MODEL_PATH]
optional arguments:
--patch_size size of adversarial patch
--alpha $\alpha$ in paper
--beta $\beta$ in paper
--exp exp name in logging
--epochs epochs for training
--lr_gen learning rate
--batch_size batch size
--device cuda or cpu
- Adversarial training with GDPA-AT:
python gdpa_at.py --data_path [FOLDER_NAME] --vgg_model_path [MODEL_PATH]
optional arguments:
--patch_size size of adversarial patch
--beta $\beta$ in paper
--lr_gen learning rate for generator
--lr_clf learning rate for classifier
--save_freq frequency of saving the model
--epochs epochs for training
--batch_size batch size
--device cuda or cpu
--enable_testing testing during training
- Visulize ASRs and adversarial images with tensorboard:
tensorboard --logdir logs/exp/gdpa/
tensorboard --logdir logs/exp/gdpa_at/
If you find this repository useful, please cite our paper:
@article{xiang2021gdpa,
title={Generative Dynamic Patch Attack},
author={Xiang Li and Shihao Ji},
journal={British Machine Vision Conference (BMVC)},
year={2021}
}