# Hardened Extension of the Adversarial Robustness Toolbox (HEART)
HEART is a Python extension library for Machine Learning Security that builds on the popular Adversarial Robustness algorithms within the [Adversarial Robustness Toolbox (ART)](https://github.com/Trusted-AI/adversarial-robustness-toolbox). 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](https://heart-library.readthedocs.io/).
- Support for T&E of models for Department of Defense use cases
- Alignment to [MAITE](https://github.com/mit-ll-ai-technology/maite) protocols for seamless T&E workflows
- Essential subset of adversarial robustness methods for targeted AI security coverage
- Quality assurance of model assessments in the form of metadata
- In-depth support for users based on codified T&E expert experience in form of guides and examples
- Front-end application for low-code users: HEART Gradio Application
# Installation
### From Python Packaging Index (PyPI)
To install the latest version of HEART from PyPI, run:
```shell
pip install heart-library
```
### From IBM GitHub Source
To install the latest version of HEART from the [heart-library public GitHub](https://github.com/IBM/heart-library), run:
```shell
git clone https://github.com/IBM/heart-library.git
cd heart-library
pip install .
```
### (Optional) Development Environment via Poetry
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:
- [Poetry Installation Instructions](https://github.com/IBM/heart-library/blob/main/poetry_installation.md)
# Getting Started With HEART
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:
- [HEART Jupyter Notebooks](https://github.com/IBM/heart-library/tree/main/notebooks)
# HEART Modules
The HEART library is organized into three primary modules: attacks, estimators, and metrics.
### heart_library.attacks
> The HEART attacks module contains implementations of attack algorithms for generating adversarial examples and evaluating model robustness.
### heart_library.estimators
> The HEART estimators module contains classes that wrap and extend the evaluated model to make it compatible with attacks and metrics.
### heart_library.metrics
> The HEART metrics module implements industry standard, commonly-used T&E metrics for model evaluation.
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"description": "# Hardened Extension of the Adversarial Robustness Toolbox (HEART) \n\n![Static Badge](https://img.shields.io/badge/python-3.9%20--%203.11-blue \"Python 3.9 - 3.11 version support.\")\n\nHEART is a Python extension library for Machine Learning Security that builds on the popular Adversarial Robustness algorithms within the [Adversarial Robustness Toolbox (ART)](https://github.com/Trusted-AI/adversarial-robustness-toolbox). 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](https://heart-library.readthedocs.io/). \n\n- Support for T&E of models for Department of Defense use cases \n- Alignment to [MAITE](https://github.com/mit-ll-ai-technology/maite) protocols for seamless T&E workflows\n- Essential subset of adversarial robustness methods for targeted AI security coverage \n- Quality assurance of model assessments in the form of metadata \n- In-depth support for users based on codified T&E expert experience in form of guides and examples\n- Front-end application for low-code users: HEART Gradio Application \n\n# Installation\n\n### From Python Packaging Index (PyPI)\n\nTo install the latest version of HEART from PyPI, run:\n\n```shell\npip install heart-library\n```\n\n### From IBM GitHub Source\n\nTo install the latest version of HEART from the [heart-library public GitHub](https://github.com/IBM/heart-library), run:\n\n```shell\ngit clone https://github.com/IBM/heart-library.git\ncd heart-library\npip install .\n```\n\n### (Optional) Development Environment via Poetry\n\nIn 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:\n\n- [Poetry Installation Instructions](https://github.com/IBM/heart-library/blob/main/poetry_installation.md)\n\n# Getting Started With HEART\n\nIBM 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:\n\n- [HEART Jupyter Notebooks](https://github.com/IBM/heart-library/tree/main/notebooks)\n\n# HEART Modules\n\nThe HEART library is organized into three primary modules: attacks, estimators, and metrics.\n\n### heart_library.attacks\n\n> The HEART attacks module contains implementations of attack algorithms for generating adversarial examples and evaluating model robustness.\n\n### heart_library.estimators\n\n> The HEART estimators module contains classes that wrap and extend the evaluated model to make it compatible with attacks and metrics.\n\n### heart_library.metrics\n\n> The HEART metrics module implements industry standard, commonly-used T&E metrics for model evaluation.\n\n",
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