This repository is an implementation of https://link.springer.com/chapter/10.1007/978-3-030-72699-7_35 article. It uses evolutionary strategy to configure image filter parameters in order to attack adversarially to a neural network.
For image filters, we've been using the implementations from https://github.com/ruvvet/opencv_filters and gingham from the library pilgram. There were some modifications for image filters to configure the parameters mentioned in the article. Also a method named feature squeezing is being used which the code is from the owner of it in https://github.com/mzweilin/EvadeML-Zoo/blob/master/Reproduce_FeatureSqueezing.md
pip install -r requirements.txtmkdir results- either run one of these, with change of configuaration in them (if you want)
python main.pypython main.ipynb
Note: This project is using just the 100 first images in training set of CIFAR-10 because of low computational resources available, but you can increase the images using the image_portion initial variable in fitness.py.