Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.contributor.authorLi, Ying
dc.contributor.authorBian, Shan
dc.contributor.authorWang, Chuntao
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2023-09-07T07:11:40Z
dc.date.available2023-09-07T07:11:40Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported by the National Natural Science Foun-dation of China (62172165) ; the Science and Technology Program of Guangzhou (202102020582) ; the Natural Science Foundation of Guangdong Province (2022A1515010325) ; the Guangzhou Basic and Applied Basic Research Project (202201010742) .en_US
dc.description.abstractThe increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumors. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models. The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.en_US
dc.description.sponsorshipNational Natural Science Foun-dation of China [62172165]; Science and Technology Program of Guangzhou [202102020582]; Natural Science Foundation of Guangdong Province [2022A1515010325]; Guangzhou Basic and Applied Basic Research Project [202201010742]en_US
dc.identifier.citationLi, Y., Bian, S., Wang, C., Polat, K., Alhudhaif, A., & Alenezi, F. (2023). Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework. Expert Systems with Applications, 120646.en_US
dc.identifier.doi10.1016/j.eswa.2023.120646
dc.identifier.endpage14en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85162054325en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2023.120646
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11671
dc.identifier.volume231en_US
dc.identifier.wosWOS:001020457900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeepfake Detectionen_US
dc.subjectVideo Forensicsen_US
dc.subjectSuper Resolutionen_US
dc.subjectAttention Mechanismen_US
dc.subjectFaceen_US
dc.subjectImagesen_US
dc.titleExposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Frameworken_US
dc.typeArticleen_US

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