Comprehensive Multiparametric Analysis of Human Deepfake Speech Recognition

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dc.contributor.authorMalinka, Kamilcs
dc.contributor.authorFirc, Antoncs
dc.contributor.authorŠalko, Milancs
dc.contributor.authorPrudký, Danielcs
dc.contributor.authorRadačovská, Karolínacs
dc.contributor.authorHanáček, Petrcs
dc.coverage.issue24cs
dc.coverage.volume2024cs
dc.date.issued2024-08-30cs
dc.description.abstractIn this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills.en
dc.description.abstractIn this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills.en
dc.formattextcs
dc.format.extent1-25cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationEURASIP Journal on Image and Video Processing. 2024, vol. 2024, issue 24, p. 1-25.en
dc.identifier.doi10.1186/s13640-024-00641-4cs
dc.identifier.issn1687-5176cs
dc.identifier.orcid0000-0002-9009-2193cs
dc.identifier.orcid0000-0002-4717-1910cs
dc.identifier.orcid0009-0004-9604-168Xcs
dc.identifier.orcid0000-0001-5507-0768cs
dc.identifier.other189344cs
dc.identifier.researcheridAAB-5046-2022cs
dc.identifier.researcheridHJP-8074-2023cs
dc.identifier.scopus24824985000cs
dc.identifier.scopus57699371300cs
dc.identifier.scopus6508388287cs
dc.identifier.urihttp://hdl.handle.net/11012/252348
dc.language.isoencs
dc.publisherSpringer Naturecs
dc.relation.ispartofEURASIP Journal on Image and Video Processingcs
dc.relation.urihttps://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00641-4cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1687-5176/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectDeepfakeen
dc.subjectSynthetic speechen
dc.subjectDeepfake detectionen
dc.subjectHuman perceptionen
dc.subjectSpeech qualityen
dc.subjectCybersecurity<br>en
dc.subjectDeepfake
dc.subjectSynthetic speech
dc.subjectDeepfake detection
dc.subjectHuman perception
dc.subjectSpeech quality
dc.subjectCybersecurity<br>
dc.titleComprehensive Multiparametric Analysis of Human Deepfake Speech Recognitionen
dc.title.alternativeComprehensive Multiparametric Analysis of Human Deepfake Speech Recognitionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189344en
sync.item.dbtypeVAVen
sync.item.insts2025.10.14 14:13:17en
sync.item.modts2025.10.14 09:47:28en
thesis.grantorVysoké učení technické v Brně. Fakulta informačních technologií. Ústav inteligentních systémůcs
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