A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges

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aut.relation.endpage585
aut.relation.issue3
aut.relation.journalElectronics (Switzerland)
aut.relation.startpage585
aut.relation.volume13
dc.contributor.authorGong, LY
dc.contributor.authorLi, XJ
dc.date.accessioned2024-02-20T03:19:54Z
dc.date.available2024-02-20T03:19:54Z
dc.date.issued2024-01-31
dc.description.abstractDeepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Recent years have seen widespread facial forgery, which does not require technical skills. Since the development of generative adversarial networks (GANs) and diffusion models (DMs), deepfake generation has been moving toward better quality. Therefore, it is necessary to find an effective method to detect fake media. This contemporary survey provides a comprehensive overview of several typical facial forgery detection methods proposed from 2019 to 2023. We also analyze and group them into four categories in terms of their feature extraction methods and network architectures: traditional convolutional neural network (CNN)-based detection, CNN backbone with semi-supervised detection, transformer-based detection, and biological signal detection. Furthermore, it summarizes several representative deepfake detection datasets with their advantages and disadvantages. Finally, we evaluate the performance of these detection models with respect to different datasets by comparing their evaluating metrics. Across all experimental results on these state-of-the-art detection models, we find that the accuracy is largely degraded if we utilize cross-dataset evaluation. These results will provide a reference for further research to develop more reliable detection algorithms.
dc.identifier.citationElectronics (Switzerland), ISSN: 2079-9292 (Print); 2079-9292 (Online), MDPI AG, 13(3), 585-585. doi: 10.3390/electronics13030585
dc.identifier.doi10.3390/electronics13030585
dc.identifier.issn2079-9292
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10292/17239
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/2079-9292/13/3/585
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4009 Electronics, Sensors and Digital Hardware
dc.subject0906 Electrical and Electronic Engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.titleA Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges
dc.typeJournal Article
pubs.elements-id537503
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