Adversarial Machine Learning: Attacks From Laboratories to the Real World

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 ma...

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Bibliographic Details
Published inComputer (Long Beach, Calif.) Vol. 54; no. 5; pp. 56 - 60
Main Authors Lin, Hsiao-Ying, Biggio, Battista
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adversarial machine learning
Algorithms
Artificial intelligence
Biological system modeling
Data models
Machine learning
Perturbation
Security
Training data
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Summary: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.
ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2021.3057686