Adversarial Robustness Toolbox

Adversarial Robustness Toolbox (ART) is an open-source Python library for evaluating and improving the security and robustness of machine learning models against adversarial threats. It provides tools for testing models against adversarial attacks and developing defense techniques.

Features of Adversarial Robustness Toolbox:

• Provides tools for evaluating the robustness of machine learning models.
• Supports adversarial attack techniques for testing model security.
• Provides defense methods to help improve model robustness.
• Supports machine learning security research and experimentation.
• Can be used to analyze adversarial examples, model extraction, poisoning, and inference threats.

Adversarial Robustness Toolbox usage:

$ sudo su
$ apt update
$ cd /opt/adversarial-robustness-toolbox
$ source /opt/adversarial-robustness-toolbox/venv/bin/activate

$ verify installation:

# python -c "import art; print(art.**version**)"

Disclaimer: Adversarial Robustness Toolbox (ART) is an open-source project developed and maintained by its respective contributors. It is provided as a machine learning security and robustness testing tool. Users are responsible for configuring and using the software in accordance with their organizational requirements, applicable licenses, security practices, and relevant terms and policies.