This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack".

Related tags

Deep Learninggdpa
Overview

Generative Dynamic Patch Attack

This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack".

Requirements

PyTorch >= 1.6.0

TensorBoard >= 2.2.1

tqdm

Quick Start

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:

http://www.image-net.org/


  1. 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
  1. 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
  1. Visulize ASRs and adversarial images with tensorboard:
tensorboard --logdir logs/exp/gdpa/
tensorboard --logdir logs/exp/gdpa_at/

Citation

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}
}
Owner
Xiang Li
Ph.D student.
Xiang Li
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