Shypd.aiThe largest open index of AI toolsauto-attack is an open-source tool for evaluating the adversarial robustness of machine learning models. It uses an ensemble of diverse, parameter-free attacks to reliably assess model vulnerabilities.
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auto-attack is an open-source Python library designed for the reliable evaluation of adversarial robustness in machine learning models. It employs an ensemble of four diverse, parameter-free attacks: APGD-CE, APGD-DLR, FAB, and Square Attack. This approach ensures a comprehensive assessment of model vulnerabilities without requiring extensive hyperparameter tuning. The tool supports both PyTorch and TensorFlow models, providing adapters for seamless integration. It offers standard and more expensive evaluation versions, as well as options for randomized defenses and custom attack configurations. auto-attack is widely used as a standard evaluation benchmark in research, including the RobustBench leaderboard, and provides access to a Model Zoo of robust classifiers.
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