AdversariaLib is an open-source python library for the security evaluation of machine learning (ML)-based classifiers under adversarial attacks. It comes with a set of powerful features:

• Easy-to-use. Running sophisticated experiments is as easy as launching a single script. Experimental settings can be defined through a single setup file.
• Wide range of supported ML algorithms. All supervised learning algorithms supported by scikit-learn are available, as well as Neural Networks (NNs), by means of our scikit-learn wrapper for FANN. In the current implementation, the library allows for the security evaluation of SVMs with linear, rbf, and polynomial kernels, and NNs with one hidden layer, against evasion attacks.
• Fast Learning and Evaluation. Thanks to scikit-learn and FANN, all supported ML algorithms are optimized and written in C/C++ language.
• Built-in attack algorithms. Evasion attacks based on gradient-descent optimization.
• Extensible. Other attack algorithms can be easily added to the library.
• Multi-processing. Do you want to further save time? The built-in attack algorithms can run concurrently on multiple processors.

Last, but not least, AdversariaLib is free software, released under the GNU GPL version 3!