Adversarial Machine Learning: Attacks from Laboratories to the Real World / Hsiao–Ying Lin; Battista Biggio
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© 1970–2012 IEEE.Adversarial machine learning (AML) is a recent research field that investigates potential security issues related to the use of machine learning (ML) algorithms in modern artificial intelligence (AI)–based systems, along with defensive techniques to protect ML algorithms against such threats. The main threats against ML encompass a set of techniques that aim to mislead ML models through adversarial input perturbations. Unlike ML–enabled crimes, in which ML is used for malicious and offensive purposes, and ML–enabled security mechanisms, in which ML is used for securing existing systems, AML techniques exploit and specifically address the security vulnerabilities of ML algorithms.
