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secml: A Python Library for Secure and Explainable Machine Learning. (arXiv:1912.10013v2 [cs.LG] UPDATED)
May 16, 2022, 1:20 a.m. | Maura Pintor, Luca Demetrio, Angelo Sotgiu, Marco Melis, Ambra Demontis, Battista Biggio
cs.CR updates on arXiv.org arxiv.org
We present \texttt{secml}, an open-source Python library for secure and
explainable machine learning. It implements the most popular attacks against
machine learning, including test-time evasion attacks to generate adversarial
examples against deep neural networks and training-time poisoning attacks
against support vector machines and many other algorithms. These attacks enable
evaluating the security of learning algorithms and the corresponding defenses
under both white-box and black-box threat models. To this end, \texttt{secml}
provides built-in functions to compute security evaluation curves, showing how …
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