  We present AdversariaLib, an open-source python library for the security
evaluation of machine learning (ML) against carefully-targeted attacks. It
supports the implementation of several attacks proposed thus far in the
literature of adversarial learning, allows for the evaluation of a wide range
of ML algorithms, runs on multiple platforms, and has multi-processing enabled.
The library has a modular architecture that makes it easy to use and to extend
by implementing novel attacks and countermeasures. It relies on other
widely-used open-source ML libraries, including scikit-learn and FANN.
Classification algorithms are implemented and optimized in C/C++, allowing for
a fast evaluation of the simulated attacks. The package is distributed under
the GNU General Public License v3, and it is available for download at
http://sourceforge.net/projects/adversarialib.
