Adversarial Machine Learning

Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar

Abstract

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning - the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

Details

Publication typeInproceedings
Published inProceedings of the 4th ACM Workshop on Artificial Intelligence and Security
Pages43-58
PublisherACM
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