Black-Box Adversarial Attacks for Image Classifiers

• attacks contains all the classes encapsulating various BB attacks: MAB, epsilon-greedy, EvoBA, (our version of) SimBA, etc
• models contains models artifacts (usually, most will be left out because of their big dimensions)
• notebooks contains multiple experiments we've been running (in the ipynb format). Most notably, the cifar_10_experiments.ipynb and imagenet_experiments.ipynb were used in the EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks paper experiments
• utils contains various helper functions
• run_evoba.py is a simple script that runs EvoBA against custom models and tasks. Sample usage:

python run_robustness_checks.py
    --model_path "models/cifar100vgg/cifar100vgg.py" 
    --model_class_name "cifar100vgg" 
    --task "cifar100" 
    --sample_size 100

As there are multiple experiments and attacks in this repo, we provide a pack with the minimal necessary scripts and notebooks to replicate the EvoBA results from EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks. This pack includes the used data and models as well. It can be found here: https://drive.google.com/file/d/1OVn3w0VBtW5x84LTsJUymgqYlnJKwCtA/view?usp=sharing