secml: A Python Library for Secure and Explainable Machine Learning

Understand

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 quickly classification performance decreases against increasing adversarial perturbations of the input data.