Adversarial Robustness Toolbox

Adversarial Robustness Toolbox (ART)

The Adversarial Robustness Toolbox (ART) is a Python library aimed at improving the security of machine learning models against various adversarial threats. With support for a plethora of machine learning frameworks, ART provides a robust set of tools for developers and researchers alike.

Key Features:

• Versatile Framework Support: Compatible with TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, and GPy.
• Comprehensive Attack and Defense Strategies: Provides resources for Evasion, Poisoning, Extraction, and Inference attacks along with corresponding defense mechanisms.
• Wide Range of Data Types: Capable of handling different data types including images, tables, audio, and video.
• Various ML Tasks Supported: Facilitates classification, object detection, speech recognition, and model evaluation, among other tasks.

Benefits:

• Continuous Development: Regular updates and improvements, inviting community contributions and user feedback.
• Research and Practical Applications: Suitable for both academic research and practical deployment in security-sensitive environments.

Highlights:

ART's continuous commitment to fortifying machine learning applications against adversarial threats makes it an essential tool for Red and Blue Teams, ensuring safer AI implementations.

For collaboration and contributions, check out the GitHub repository.