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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27053
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dc.contributor.authorYang, J-
dc.contributor.authorSun, Y-
dc.contributor.authorMao, M-
dc.contributor.authorBai, L-
dc.contributor.authorZhang, S-
dc.contributor.authorWang, F-
dc.date.accessioned2023-08-25T07:18:26Z-
dc.date.available2023-08-25T07:18:26Z-
dc.date.issued2023-06-08-
dc.identifierORCID iDs: Jun Yang https://orcid.org/0000-0002-2124-0869; Yaoru Sun https://orcid.org/0000-0002-2179-0713; Fang Wang https://orcid.org/0000-0003-1987-9150.-
dc.identifier.citationYang, J. et al. (2023) 'Model-agnostic Method: Exposing Deepfake using Pixel-wise Spatial and Temporal Fingerprints', IEEE Transactions on Big Data, 9 (6), pp. 1496 - 1509. doi: 10.1109/tbdata.2023.3284272.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/27053-
dc.description.sponsorshipNational Key R&D Program of China (Grant Number: 2019YFC1906201); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 91748122); Xianyang Key R&D Program (Grant Number: S2021ZDYF-SF-0739).en_US
dc.format.extent1496 - 1509-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html-
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html-
dc.subjectdeepfake detectionen_US
dc.subjectphotoplethysmography (PPG)en_US
dc.subjectauto-regressive (AR)en_US
dc.subjecttemporal and spatialen_US
dc.subjectfingerprinten_US
dc.subjectdeep learningen_US
dc.titleModel-agnostic Method: Exposing Deepfake using Pixel-wise Spatial and Temporal Fingerprintsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/tbdata.2023.3284272-
dc.relation.isPartOfIEEE Transactions on Big Data-
pubs.issue6-
pubs.publication-statusPublished-
pubs.volume9-
dc.identifier.eissn2332-7790-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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