Query-efficient Meta Aattack to Deep Neural Networks

This repository contains the code for reproducing the experimental results of attacking mnist, cifar10, tiny-imagenet models, of our submission: Query-efficient Meta Aattack to Deep Neural Networks (openreview). The paper can be cited as follows:

@inproceedings{
Du2020Query-efficient,
title={Query-efficient Meta Attack to Deep Neural Networks},
author={Jiawei Du and Hu Zhang and Joey Tianyi Zhou and Yi Yang and Jiashi Feng},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Skxd6gSYDS}
}

Reproducing the results (take cifar10 for example)

Requirements

• Pytorch (torch, torchvision) packages

  For generating gradients for meta model training

  cd gen_grad/cifar_gen_grad_for_meta

python cifar_main.py

For training meta model to attack

cd meta_training/cifar_meta_training

python cifar_train.py

For query-efficient attack

The results can be reproduced (with the default hyperparameters) with the following command:

cd meta_attack/meta_attack_cifar

python test_all.py

The models we used for meta attacker training and final attack can be found here.