Secured convolutional layer hardware co-processor in convolutional neural network (CNN) using facial biometric

Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12737

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DC Field	Value	Language
dc.contributor.author	Sengupta, Anirban	en_US
dc.contributor.author	Chaurasia, Rahul	en_US
dc.date.accessioned	2023-12-14T12:38:21Z	-
dc.date.available	2023-12-14T12:38:21Z	-
dc.date.issued	2023	-
dc.identifier.citation	Sengupta, A., & Chaurasia, R. (2023d). Methodology for exploration of security-design cost trade-off for signature-based security algorithms. In Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors. Institution of Engineering and Technology	en_US
dc.identifier.citation	Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167360063&partnerID=40&md5=115c5d40914d3d6a1a6349f679db7899	en_US
dc.identifier.isbn	978-1839538216	-
dc.identifier.other	EID(2-s2.0-85167351140)	-
dc.identifier.uri	https://dspace.iiti.ac.in/handle/123456789/12737	-
dc.description.abstract	[No abstract available]	en_US
dc.language.iso	en	en_US
dc.publisher	Institution of Engineering and Technology	en_US
dc.source	Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors	en_US
dc.title	Secured convolutional layer hardware co-processor in convolutional neural network (CNN) using facial biometric	en_US
dc.type	Book Chapter	en_US
Appears in Collections:	Department of Computer Science and Engineering

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