Adversarial machine learning

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.

# Adversarial Robustness in Machine Learning # Computer Science # Privacy-Preserving Technologies in Data # Security and Verification in Computing

Ling Huang, Anthony D. Joseph, Blaine A. Nelson, et al. (2011). "Adversarial machine learning". ACM Workshop on Artificial Intelligence and Security (AISec). https://doi.org/10.1145/2046684.2046692