ZO-BCD

This repository is for our paper:

[1] HanQin Cai, Yuchen Lou, Daniel Mckenzie, and Wotao Yin. A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization. In International Conference on Machine Learning, 2021.

Imagenet Attack

To replicate our imagenet attacks do the following:

1. You will need the Wavelet toolbox and the Deep Learning toolbox installed on Matlab.
2. Download the pre-trained Inceptionv3 model, as described here.
3. Download the ImageNet test set, available here. Copy this folder into Image Attack. (Alternatively, update the path in ZOBCD_Image_Attack.m file).
4. That's it! Run ZOBCD_Image_Attack.m to perform the attacks. Note that we have included a few sample outputs in Examples.

Audio Attack

To replicate our audio attacks do the following:

1. You will need the Audio, Wavelet, and Deep Learning toolboxes in Matlab installed (need the version to be at least 2019b for Audio toolbox).
2. Follow the instructions here to download the SpeechCommands data set. Put the folders in Audio Attack/Sounds. This path can be changed in ZO_BCD_Audio_Targeted_Test.m.
3. Open the MATLAB Deep Learning Speech Recognition Example folder (as described here ). You will need to copy commandNet.mat and helperExtractAuditoryFeatures.m into Audio Attack .
4. That's it! You can now run ZO_BCD_Audio_Targeted_Test.m and see the results. Note that we have included a few sample outputs in Examples.

Generic ZO-BCD

We provide both MATLAB and Python Implementations of the generic ZO-BCD in the folders matlabZO-BCD and pyZO-BCD respectively. We use the MATLAB version for the sythetic experiments in our paper. Run Test_ZOBCD.m in the folder matlabZO-BCD to get a general comparsion between two variants of ZO-BCD: ZO-BCD-R and ZO-BCD-RC.

Notice that we use MATLAB's "run and time" for the time experiment in our paper.