A review of deep learning-based approaches for deepfake content detection

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dc.contributor.authorPassos, Leandro A.
dc.contributor.authorJodas, Danilo
dc.contributor.authorCosta, Kelton A.P.
dc.contributor.authorSouza Júnior, Luis A.
dc.contributor.authorRodrigues, Douglas
dc.contributor.authorDel Ser, Javier
dc.contributor.authorCamacho, David
dc.contributor.authorPapa, João Paulo
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T10:40:04Z
dc.date.available2024-09-10T10:40:04Z
dc.date.issued2024
dc.descriptionPublisher Copyright: © 2024 John Wiley & Sons Ltd.
dc.description.abstractRecent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.en
dc.description.statusPeer reviewed
dc.identifier.citationPassos , L A , Jodas , D , Costa , K A P , Souza Júnior , L A , Rodrigues , D , Del Ser , J , Camacho , D & Papa , J P 2024 , ' A review of deep learning-based approaches for deepfake content detection ' , Expert Systems . https://doi.org/10.1111/exsy.13570
dc.identifier.doi10.1111/exsy.13570
dc.identifier.issn0266-4720
dc.identifier.urihttps://hdl.handle.net/11556/4973
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85186486979&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofExpert Systems
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsdeep learning
dc.subject.keywordsfake content
dc.subject.keywordsmachine learning
dc.subject.keywordssecurity
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsComputational Theory and Mathematics
dc.subject.keywordsArtificial Intelligence
dc.titleA review of deep learning-based approaches for deepfake content detectionen
dc.typejournal article
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