A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models

dc.authoridUnal, Mehmet/0000-0003-1243-153X
dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authorscopusid57217424609
dc.authorscopusid55897274300
dc.authorscopusid6507328166
dc.authorscopusid58564751100
dc.authorscopusid57254381700
dc.authorwosidUnal, Mehmet/W-2804-2018
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.contributor.authorDağ, Hasan
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorDag, Hasan
dc.contributor.authorTalebi, Samira
dc.contributor.authorUnal, Mehmet
dc.date.accessioned2024-06-23T21:37:27Z
dc.date.available2024-06-23T21:37:27Z
dc.date.issued2024
dc.departmentKadir Has Universityen_US
dc.department-temp[Heidari, Arash] Halic Univ, Dept Software Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Yunlin 64002, Taiwan; [Dag, Hasan] Kadir Has Univ, Dept Informat Technol, Istanbul, Turkiye; [Talebi, Samira] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX USA; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiyeen_US
dc.descriptionUnal, Mehmet/0000-0003-1243-153X; Heidari, Arash/0000-0003-4279-8551en_US
dc.description.abstractIn recent years, the proliferation of deep learning (DL) techniques has given rise to a significant challenge in the form of deepfake videos, posing a grave threat to the authenticity of media content. With the rapid advancement of DL technology, the creation of convincingly realistic deepfake videos has become increasingly prevalent, raising serious concerns about the potential misuse of such content. Deepfakes have the potential to undermine trust in visual media, with implications for fields as diverse as journalism, entertainment, and security. This study presents an innovative solution by harnessing blockchain-based federated learning (FL) to address this issue, focusing on preserving data source anonymity. The approach combines the strengths of SegCaps and convolutional neural network (CNN) methods for improved image feature extraction, followed by capsule network (CN) training to enhance generalization. A novel data normalization technique is introduced to tackle data heterogeneity stemming from diverse global data sources. Moreover, transfer learning (TL) and preprocessing methods are deployed to elevate DL performance. These efforts culminate in collaborative global model training zfacilitated by blockchain and FL while maintaining the utmost confidentiality of data sources. The effectiveness of our methodology is rigorously tested and validated through extensive experiments. These experiments reveal a substantial improvement in accuracy, with an impressive average increase of 6.6% compared to six benchmark models. Furthermore, our approach demonstrates a 5.1% enhancement in the area under the curve (AUC) metric, underscoring its ability to outperform existing detection methods. These results substantiate the effectiveness of our proposed solution in countering the proliferation of deepfake content. In conclusion, our innovative approach represents a promising avenue for advancing deepfake detection. By leveraging existing data resources and the power of FL and blockchain technology, we address a critical need for media authenticity and security. As the threat of deepfake videos continues to grow, our comprehensive solution provides an effective means to protect the integrity and trustworthiness of visual media, with far-reaching implications for both industry and society. This work stands as a significant step toward countering the deepfake menace and preserving the authenticity of visual content in a rapidly evolving digital landscape.en_US
dc.description.sponsorshipKadir Has Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.citation3
dc.identifier.doi10.1007/s12559-024-10255-7
dc.identifier.endpage1091en_US
dc.identifier.issn1866-9956
dc.identifier.issn1866-9964
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85183091123
dc.identifier.scopusqualityQ1
dc.identifier.startpage1073en_US
dc.identifier.urihttps://doi.org/10.1007/s12559-024-10255-7
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5721
dc.identifier.volume16en_US
dc.identifier.wosWOS:001148512000002
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBlockchainen_US
dc.subjectConvolutional neural networken_US
dc.subjectDeepfakeen_US
dc.subjectTransfer learningen_US
dc.subjectQoSen_US
dc.subjectPrivacyen_US
dc.subjectFederated learningen_US
dc.titleA Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicatione02bc683-b72e-4da4-a5db-ddebeb21e8e7
relation.isAuthorOfPublication.latestForDiscoverye02bc683-b72e-4da4-a5db-ddebeb21e8e7

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