GradMDM

This repository contains the result and the sample code for the work: GradMDM: Adversarial Attack on Dynamic Networks

To perturb adversarial samples to SkipNet on the ImageNet validation dataset

Prerequisite

1. We support training with Pytorch 1.10.0. To install required packages

conda install pytorch=1.10 torchvision cudatoolkit=<the CUDA version you want> numpy

2. To prepare ImageNet dataset, please follow this link.

3. To prepare SkipNet pretrained model, please follow this link.

Training

1. To train the adversarial samples with $gamma=100$, run

python -u train_gradmdm.py --model-type rl --gamma 100

2. To train the adversarial samples without accuracy drop, run

python -u train_gradmdm.py --model-type rl --gamma 100 --acc-maintain

Citation

If you find our project useful in your research, please consider citing:

@article{pan2023gradmdm,
  title={GradMDM: Adversarial Attack on Dynamic Networks},
  author={Pan, Jianhong and Foo, Lin Geng and Zheng, Qichen and Fan, Zhipeng and Rahmani, Hossein and Ke, Qiuhong and Liu, Jun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  publisher={IEEE}
}