K-Anonymity inspired adversarial attack and multiple one-class classification defense.

Adversarial attack Adversarial defense Deep SVDD K-Anonymity Kernel learning

Journal

Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Titre abrégé: Neural Netw
Pays: United States
ID NLM: 8805018

Informations de publication

Date de publication:
Apr 2020
Historique:
received: 26 03 2019
revised: 23 12 2019
accepted: 14 01 2020
pubmed: 10 2 2020
medline: 25 8 2020
entrez: 10 2 2020
Statut: ppublish

Résumé

A novel adversarial attack methodology for fooling deep neural network classifiers in image classification tasks is proposed, along with a novel defense mechanism to counter such attacks. Two concepts are introduced, namely the K-Anonymity-inspired Adversarial Attack (K-A

Identifiants

pubmed: 32036227
pii: S0893-6080(20)30017-4
doi: 10.1016/j.neunet.2020.01.015
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

296-307

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Vasileios Mygdalis (V)

Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: mygdalisv@csd.auth.gr.

Anastasios Tefas (A)

Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: tefas@csd.auth.gr.

Ioannis Pitas (I)

Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: pitas@csd.auth.gr.

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Classifications MeSH