An Enhanced STFT Segmentation Framework for ENF-Based Media Forensics

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dc.contributor.authorBerk Yalinkilic, Ali
dc.contributor.authorVatansever, Saffet
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe electric network frequency (ENF) criterion has gained significant attention over the past two decades as a promising tool in digital media forensics. ENF is the frequency of the alternating current (AC) signal in a mains electricity network, exhibiting continual fluctuations within certain limits around a nominal frequency, contingent upon supplied and demanded power disparities. A sequence of ENF alterations is called an ENF signal, which is inherently embedded in audio and video recordings under certain circumstances. Several efforts have been made to accurately estimate the ENF signal from media. However, no matter how accurately estimated, a media ENF signal may not be reliably used in forensic applications unless sufficiently distinctive. To clarify, ENF may show similar fluctuation patterns at different time intervals. These patterns become more distinct over longer periods of time. Accordingly, working with as large an ENF signal as possible is critical for reliability. To achieve an extended and, thus, more distinctive ENF signal, this study proposes a smart segmentation scheme for Short-Time Fourier Transform (STFT)-based ENF estimation, which derives more data segments from a given media than the conventional STFT technique, leading to increased ENF estimates for any specified STFT parameter setting. The proposed approach can be combined with any ENF accuracy enhancement strategy to obtain relatively more reliable signals. Large-scale tests conducted with different STFT parameters and audio clip lengths showed that the proposed scheme can efficiently improve the performance when used alone or in conjunction with other ENF enhancement strategies.
dc.description.sponsorshipBursa Technical University Scientific Research Units [211N022]
dc.description.sponsorshipThis work was supported in part by Bursa Technical University Scientific Research Units under Project 211N022.
dc.identifier.doi10.1109/ACCESS.2024.3449099
dc.identifier.endpage117862
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85201759668
dc.identifier.scopusqualityQ1
dc.identifier.startpage117850
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3449099
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5890
dc.identifier.volume12
dc.identifier.wosWOS:001303366300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectMedia
dc.subjectEstimation
dc.subjectForensics
dc.subjectStandards
dc.subjectFiltering theory
dc.subjectFast Fourier transforms
dc.subjectSupply and demand
dc.subjectENF
dc.subjectelectric network frequency
dc.subjectmedia forensics
dc.subjectshort-time Fourier transform
dc.subjectSTFT segmentation
dc.subjecttime-of-recording
dc.subjecttimestamp
dc.titleAn Enhanced STFT Segmentation Framework for ENF-Based Media Forensics
dc.typeArticle

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