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Adversarial Machine Learning: Attacks From Laboratories to the Real World.
- Source :
-
Computer (00189162) . May2021, Vol. 54 Issue 5, p56-60. 5p. - Publication Year :
- 2021
-
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]
Details
- Language :
- English
- ISSN :
- 00189162
- Volume :
- 54
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Computer (00189162)
- Publication Type :
- Academic Journal
- Accession number :
- 150287600
- Full Text :
- https://doi.org/10.1109/MC.2021.3057686