Adversarial Robustness Toolbox
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Python library for Machine Learning Security; provides tools to defend and evaluate Machine Learning models and applications against adversarial threats of Evasion, Poisoning, Extraction, and Inference; supports frameworks TensorFlow, Keras, PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, e
Flagship ML security library for evaluating and defending models against evasion, poisoning, extraction, and inference attacks across major ML frameworks
Used for crafting adversarial examples/perturbations against ML models
Model manipulation/evasion testing; 39 attack modules (evasion, poisoning, extraction, inference) and 29 defense modules; compatible with 10+ ML frameworks (TensorFlow, PyTorch, scikit-learn, Keras); provides robustness metrics/certification; maintained under Linux Foundation AI and Data project; do
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