ART - Adversarial Robustness Toolbox | HACK4NET

This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers.

The library is still under development. Feedback, bug reports and extension requests are highly appreciated.

The Adversarial Robustness Toolbox is designed to run with Python 3 (and most likely Python 2 with small changes). You can either download the source code or clone the repository in your directory of choice:

git clone https://github.com/IBM/adversarial-robustness-toolbox

To install the project dependencies, use the requirements file:

pip install .

The library comes with a basic set of unit tests. To check your install, you can run all the unit tests by calling in the library folder:

bash run_tests.sh

The configuration file config/config.ini allows to set custom paths for data. By default, data is downloaded in the datafolder as follows:

[DEFAULT]
profile=LOCAL

[LOCAL]
data_path=./data
mnist_path=./data/mnist
cifar10_path=./data/cifar-10
stl10_path=./data/stl-10

If the datasets are not present at the indicated path, loading them will also download the data.

Github Project