Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82244
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dc.contributorChinese Mainland Affairs Officeen_US
dc.contributorDepartment of Health Technology and Informaticsen_US
dc.creatorZhou, LNen_US
dc.creatorXiao, Yen_US
dc.creatorChen, Wen_US
dc.date.accessioned2020-05-05T05:59:15Z-
dc.date.available2020-05-05T05:59:15Z-
dc.identifier.urihttp://hdl.handle.net/10397/82244-
dc.language.isoenen_US
dc.publisherOptical Society of Americaen_US
dc.rights© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (https://www.osapublishing.org/library/license_v1.cfm#VOR-OA)en_US
dc.rights© 2020 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.en_US
dc.rightsJournal © 2020en_US
dc.rightsThe following publication Lina Zhou, Yin Xiao, and Wen Chen, "Learning-based attacks for detecting the vulnerability of computer-generated hologram based optical encryption," Opt. Express 28, 2499-2510 (2020) is available at https://dx.doi.org/10.1364/OE.380004en_US
dc.titleLearning-based attacks for detecting the vulnerability of computer-generated hologram based optical encryptionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2499en_US
dc.identifier.epage2510en_US
dc.identifier.volume28en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1364/OE.380004en_US
dcterms.abstractOptical encryption has attracted wide attention for its remarkable characteristics. Inspired by the development of double random phase encoding, many researchers have developed a number of optical encryption systems for practical applications. It has also been found that computer-generated hologram (CGH) is highly promising for optical encryption, and the CGH-based optical encryption possesses remarkable advantages of simplicity and high feasibility for practical implementations. An input image, i.e., plaintext, can be iteratively or non-iteratively encoded into one or several phase-only masks via phase retrieval algorithms. Without security keys, it is impossible for unauthorized receivers to correctly extract the input image from ciphertext. However, cryptoanalysis of CGH-based optical encryption systems has not been effectively carried out before, and it is also concerned whether CGH-based optical encryption is sufficiently secure for practical applications. In this paper, learning-based attack is proposed to demonstrate the vulnerability of CGH-based optical security system without the direct retrieval of optical encryption keys for the first time to our knowledge. Many pairs of the extracted CGH patterns and their corresponding input images (i.e., ciphertext-plaintext pairs) are used to train a designed learning model. After training, it is straightforward to directly retrieve unknown plaintexts from the given ciphertexts (i.e., phase-only masks) by using the trained learning model without subsidiary conditions. Moreover, the proposed learning-based attacks are also feasible and effective for the cryptoanalysis of CGH-based optical security systems with multiple cascaded phase-only masks. The proposed learning-based attacking method paves the way for the cryptoanalysis of CGH-based optical encryption. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreementen_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationOptics express, 2020, v. 28, no. 2, p. 2499-2510en_US
dcterms.isPartOfOptics expressen_US
dcterms.issued2020-
dc.identifier.isiWOS:000513232200135-
dc.identifier.eissn1094-4087en_US
dc.description.validate202006 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0739-n09, OA_Scopus/WOSen_US
dc.identifier.SubFormID1337-
dc.description.fundingSourceRGC-
dc.description.fundingSourceOthers-
dc.description.fundingTextRGC: 25201416-
dc.description.fundingTextOthers: G-YBVU, 4-BCDY, R2016A009, R2016A030-
dc.description.pubStatusPublisheden_US
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