swMATH ID: 21631
Software Authors: Igino Corona, Battista Biggio, Davide Maiorca
Description: AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack. 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 this http URL: https://sourceforge.net/projects/adversarialib/
Homepage: https://sourceforge.net/projects/adversarialib/
Keywords: Cryptography and Security; arXiv cs.CR; Learning; arXiv cs.LG; arXiv; adversarial learning; security evaluation; evasion attacks
Related Software: Scikit; Python
Cited in: 0 Documents