Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Interpretability and explainability of data and machine learning models

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

Paddle with Decentralized Trust based on Xuperchain

Athena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks

A self-hosted, privacy-focused RAG (Retrieval-Augmented Generation) interface for intelligent document interaction. Turn any document into a knowledge base you can chat with.

Hands on workshop material evaluating performance, fairness and robustness of models

Security protocols for estimating adversarial robustness of machine learning models for both tabular and image datasets. This package implements a set of evasion attacks based on metaheuristic optimization algorithms, and complex cost functions to give reliable results for tabular problems.