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DC Field | Value | Language |
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dc.contributor.author | Yang, J | - |
dc.contributor.author | Sun, Y | - |
dc.contributor.author | Mao, M | - |
dc.contributor.author | Bai, L | - |
dc.contributor.author | Zhang, S | - |
dc.contributor.author | Wang, F | - |
dc.date.accessioned | 2023-08-25T07:18:26Z | - |
dc.date.available | 2023-08-25T07:18:26Z | - |
dc.date.issued | 2023-06-08 | - |
dc.identifier | ORCID 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.citation | Yang, 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.uri | https://bura.brunel.ac.uk/handle/2438/27053 | - |
dc.description.sponsorship | National 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.extent | 1496 - 1509 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 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.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.subject | deepfake detection | en_US |
dc.subject | photoplethysmography (PPG) | en_US |
dc.subject | auto-regressive (AR) | en_US |
dc.subject | temporal and spatial | en_US |
dc.subject | fingerprint | en_US |
dc.subject | deep learning | en_US |
dc.title | Model-agnostic Method: Exposing Deepfake using Pixel-wise Spatial and Temporal Fingerprints | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/tbdata.2023.3284272 | - |
dc.relation.isPartOf | IEEE Transactions on Big Data | - |
pubs.issue | 6 | - |
pubs.publication-status | Published | - |
pubs.volume | 9 | - |
dc.identifier.eissn | 2332-7790 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 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 | 3.58 MB | Adobe PDF | View/Open |
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