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---
title: HEART-Gradio
emoji: 🎨
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 4.13.0
app_file: gradio/app.py
pinned: false
license: mit
---
# 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.
- 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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