A
adversarial-robustness-toolbox
Trusted-AI/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
★6.2kstars
Python
MIT
Updated: 1d ago
📋 Project at a Glance
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What's this?A adversarial-attacks/adversarial-examples tool in the Security & Governance category, built with Python, open-source
Who made it?Maintained by Trusted-AI team, 6.2K⭐ on GitHub, #36 out of 413 in Security & Governance
Why does it exist?In the Security & Governance space, adversarial-attacks workflows faced efficiency bottlenecks. adversarial-robustness-toolbox was built by Trusted-AI to address these adversarial-examples challenges.
What can it do?Key use cases: adversarial-machine-learning, artificial-intelligence, attack
How to install with AI?Use an AI coding assistant (Claude Code, Cursor, Copilot) to automatically set up pip dependencies and virtual env. Follow the README — the AI handles the rest.
🔗 github.com/Trusted-AI/adversarial-robustness-toolbox | 官网 https://adversarial-robustness-toolbox.readthedocs.io/en/latest/
🔗 github.com/Trusted-AI/adversarial-robustness-toolbox | 官网 https://adversarial-robustness-toolbox.readthedocs.io/en/latest/
Topics
adversarial-attacksadversarial-examplesadversarial-machine-learningaiartificial-intelligenceattackblue-teamevasionextractioninferencemachine-learningpoisoningprivacypythonred-teamtrusted-aitrustworthy-ai