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Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16732
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dc.contributor.authorGaniga, Prakruthi-
dc.contributor.authorPriyadarshini-
dc.contributor.authorDodda, Vineela Chandra-
dc.contributor.authorKumar, Ravi-
dc.contributor.authorMuniraj, Inbarasan-
dc.date.accessioned2024-12-12T09:29:52Z-
dc.date.available2024-12-12T09:29:52Z-
dc.date.issued2024-
dc.identifier.isbn9781957171371-
dc.identifier.urihttps://opg.optica.org/abstract.cfm?URI=ISA-2024-ITu3G.4-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16732-
dc.description.abstractAuthentication techniques can be used to overcome the hologram counterfeiting problems. Here, we demonstrate an authentication scheme for digital holograms in a raw-complex form that is stored either in the cloud or on the metasurface using a CNN. © 2024 The Author(s).en_US
dc.language.isoenen_US
dc.publisherImaging Systems and Applications, ISA 2024 in Proceedings Optica Imaging Congress 2024, 3D, AOMS, COSI, ISA, pcAOP - Part of Optica Imaging Congressen_US
dc.publisherOptical Society of Americaen_US
dc.subjectAuthenticationen_US
dc.subjectAuthentication Protocolen_US
dc.subjectElectron Holographyen_US
dc.subjectHologramsen_US
dc.subjectLithographyen_US
dc.subjectAuthentication Schemeen_US
dc.titleHologram Authentication and Classification Via a Convolutional Neural Networken_US
dc.typeArticleen_US
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