BayesOpt Attack

This is the code repository for the query-efficient black-box attack method proposed in our paper BayesOpt Adversarial Attack.

Setup

Install the required libraries:

pip install -r requirements.txt 

Collect CIFAR10 data:

python setup_cifar.py

Download Inception-v3 model checkpoint:

python setup_inception.py

Download ImageNet test data: ImageNet test images and ImageNet test labels , and put them under folder BayesOpt_Attacks/objective_func/tf_models/

Usage:

Run BayesOpt Attack experiments: python run_bayesopt_attack.py followed by the following important arguments:

• -f Attack target model/dataset: mnist, cifar10 or imagenet
• -m BayesOpt attack method: GP for GP-BO, ADDGPLD for ADDGP-BO or GPLDR for GP-BO-auto-dr
• -ld Reduced dimension. =196 for MNIST and CIFAR10, =2304 for ImageNet. Not applicable when use GP-BO-auto-dr as it learns the reduced dimension automatically
• -rd Upsampling method. Default =BILI for bilinear resizing
• -nsub Number of subspaces from decomposing the image dimensions. Only applicable when use ADDGP-BO: =14 for MNIST, =12 for CIFAR10 or =27 for ImageNet.

Other arguments are described in run_bayesopt_attack.py.

Examples

Case 1: GP-BO attack on trained MNIST classifier for 900 iterations with reduced dimension of 14x14x1. Attack 1 correctly classified image with ID=0 on the other 9 class labels:

python run_bayesopt_attack.py -f='mnist' -m='GP' -nitr=900 -rd='BILI' -ld=196 -i=0 -ntg=9

Case 2: GP-BO-auto-dr attack on trained CIFAR10 classifier for 900 iterations. Attack 1 correctly classified image with ID=0 on the other 9 class labels:

python run_bayesopt_attack.py -f='cifar10' -m='GPLDR' -nitr=900 -i=0 -ntg=9

Case 3: ADDGP-BO attack on trained ImageNet classifier for 2000 iterations with reduced dimension of 48x48x1 and 27 subspaces. Attack 1 correctly classified image with ID=0 on a random class label:

python run_bayesopt_attack.py -f='imagenet' -m='ADDGPLD' -nitr=2000 -rd='BILI' -ld=2304 -nsub=27 -i=0 -ntg=1 

Citation

Please cite our paper if you would like to use the code.

@inproceedings{
Ru2020BayesOpt,
title={BayesOpt Adversarial Attack},
author={Binxin Ru and Adam Cobb and Arno Blaas and Yarin Gal},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Hkem-lrtvH}
}