HEART is a Python extension library for Machine Learning Security that builds on the popular Adversarial Robustness algorithms within the Adversarial Robustness Toolbox (ART). The extension library allows the user to leverage core ART algorithms while providing additional benefits to AI Test & Evaluation (T&E) engineers. HEART documentation can be found here.
To install the latest version of HEART from PyPI, run:
pip install heart-library
To install the latest version of HEART from the heart-library public GitHub, run:
git clone https://github.com/IBM/heart-library.git
cd heart-library
pip install .
In some cases, it may be beneficial for developers to set up an environment from a reproducible source of truth. This environment is useful for developers that wish to work within a pull request or leverage the same development conditions used by HEART contributors. Please follow the instructions for installation via Poetry within the official HEART repository:
IBM has published a catalog of notebooks designed to assist developers of all skill levels with the process of getting started utilizing HEART in their AI T&E workflows. These Jupyter notebooks can be accessed within the official heart-library GitHub repository:
The HEART library is organized into three primary modules: attacks, estimators, and metrics.
The HEART attacks module contains implementations of attack algorithms for generating adversarial examples and evaluating model robustness.
The HEART estimators module contains classes that wrap and extend the evaluated model to make it compatible with attacks and metrics.
The HEART metrics module implements industry standard, commonly-used T&E metrics for model evaluation.