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
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
296-307Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.