The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms

Zhao, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 2nd International Conference on Computing and Data Science (CDS), 2021, 00, pp. 287-295
Issue Date:: 2021-01-29

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Field	Value	Language
dc.contributor.author	Zhao, R https://orcid.org/0000-0003-0287-0870
dc.date	2021-01-28
dc.date.accessioned	2022-07-04T20:59:26Z
dc.date.available	2022-07-04T20:59:26Z
dc.date.issued	2021-01-29
dc.identifier.citation	2021 2nd International Conference on Computing and Data Science (CDS), 2021, 00, pp. 287-295
dc.identifier.uri	http://hdl.handle.net/10453/158617
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 2nd International Conference on Computing and Data Science (CDS)
dc.relation.ispartof	2021 2nd International Conference on Computing and Data Science (CDS)
dc.relation.isbasedon	10.1109/cds52072.2021.00057
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-04T20:59:25Z
pubs.finish-date	2021-01-29
pubs.publication-status	Published
pubs.start-date	2021-01-28
pubs.volume	00

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