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 http://sourceforge.net/projects/adversarialib.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningGeneral ClassificationSimilar Papers 제목 키워드 기반
Douglas-Quaid -- Open Source Image Matching Library
Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, no open-source and turnkey library was found able to reach this goal…
ClassificationGeneral ClassificationCyNER: A Python Library for Cybersecurity Named Entity Recognition
Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python library for cybersecurity named entity recog…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NEROn using distributed representations of source code for the detection of C security vulnerabilities
This paper presents an evaluation of the code representation model Code2vec when trained on the task of detecting security vulnerabilities in C source code. We leverage the open-source library astminer to extract path-co…
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models. Amid the growing interest in big data and AI, adv…
Design principles of an open-source language modeling microservice package for AAC text-entry applications
We present MozoLM, an open-source language model microservice package intended for use in AAC text-entry applications, with a particular focus on the design principles of the library. The intent of the library is to allo…
Language ModelingLanguage Modelling