Adversarial Machine Learning

Ziv Katzir; Yuval Elovici

doi:https://doi.org/10.1007/978-3-031-24628-9_25

Adversarial Machine Learning

Ziv Katzir, Yuval Elovici

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter follows the evolution of adversarial machine learning research in recent years, through the lens of the literature. We start by reviewing early work on attack and defense methods and move on to studies that show how adversarial attacks can be applied in the real world. We then list the major outstanding research questions and conclude with research that addresses the domain’s key open question: What is it that makes adversarial examples so difficult to defend against? Our goal is to provide readers with the foundation needed to advance research in this fascinating domain.

Original language	American English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Pages	559-585
Number of pages	27
ISBN (Electronic)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_25
State	Published - 1 Jan 2023

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

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https://doi.org/10.1007/978-3-031-24628-9_25

Cite this

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Adversarial Machine Learning

Abstract

All Science Journal Classification (ASJC) codes

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