Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks

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dc.contributor.authorAhmed, S.R.
dc.contributor.authorSonuc, E.
dc.contributor.authorAhmed, M.R.
dc.contributor.authorDuru, A.D.
dc.date.accessioned2024-09-29T16:20:56Z
dc.date.available2024-09-29T16:20:56Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- Ankara -- 180434en_US
dc.description.abstractDeep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content. © 2022 IEEE.en_US
dc.identifier.doi10.1109/HORA55278.2022.9799858
dc.identifier.isbn978-166546835-0
dc.identifier.scopus2-s2.0-85133976904en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/HORA55278.2022.9799858
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9432
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAIen_US
dc.subjectauto-encodersen_US
dc.subjectDeep-fakesen_US
dc.subjectDLen_US
dc.subjectface exploitationen_US
dc.subjectforensicsen_US
dc.subjectgenerative adversarial networken_US
dc.subjectreviewen_US
dc.titleAnalysis Survey on Deepfake detection and Recognition with Convolutional Neural Networksen_US
dc.typeConference Objecten_US

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