1. Adversarial Machine Learning: Attacks From Laboratories to the Real World.
- Author
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Lin, Hsiao-Ying and Biggio, Battista
- Subjects
- *
MACHINE learning , *ARTIFICIAL intelligence , *BIOLOGICAL systems , *LABORATORIES , *ALGORITHMS - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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