ESPRIT-Hilbert-based audio tampering detection with SVM classifier for forensic analysis via electrical network frequency

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Autor(es): dc.contributorUniversity of Brasília, Department of Electrical Engineering-
Autor(es): dc.contributorNational Institute of Criminalistics, Forensic Examination Service of Electronic and Multimedia Evidences, Brasília, DF, Brazil-
Autor(es): dc.contributorUniversity of Brasília, Department of Electrical Engineering-
Autor(es): dc.contributorFraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany-
Autor(es): dc.contributorlmenau University of Technology, Institute for Information Technology, Ilmenau, Germany-
Autor(es): dc.contributorUniversity of Brasília, Department of Electrical Engineering-
Autor(es): dc.contributorlmenau University of Technology, Institute for Information Technology, Ilmenau, Germany-
Autor(es): dc.contributorFraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany-
Autor(es): dc.contributorlmenau University of Technology, Institute for Information Technology, Ilmenau, Germany-
Autor(es): dc.creatorReis, Paulo Max Gil Innocencio-
Autor(es): dc.creatorCosta, João Paulo Carvalho Lustosa da-
Autor(es): dc.creatorMiranda, Ricardo Kehrle-
Autor(es): dc.creatorDel Galdo, Giovanni-
Data de aceite: dc.date.accessioned2025-03-17T23:10:55Z-
Data de disponibilização: dc.date.available2025-03-17T23:10:55Z-
Data de envio: dc.date.issued2025-02-03-
Data de envio: dc.date.issued2025-02-03-
Data de envio: dc.date.issued2017-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/51453-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/922608-
Descrição: dc.descriptionAudio authentication is a critical task in multimedia forensics demanding robust methods to detect and identify tampered audio recordings. In this paper, a new technique to detect adulterations in audio recordings is proposed by exploiting abnormal variations in the electrical network frequency (ENF) signal eventually embedded in a questioned audio recording. These abnormal variations are caused by abrupt phase discontinuities due to insertions and suppressions of audio snippets during the tampering task. First, we propose an ESPRIT-Hilbert ENF estimator in conjunction with an outlier detector based on the sample kurtosis of the estimated ENF. Next, we use the computed kurtosis as an input for a support vector machine classifier to indicate the presence of tampering. The proposed scheme, herein designated as SPHINS, significantly outperforms related previous tampering detection approaches in the conducted tests. We validate our results using the Carioca 1 corpus with 100 unedited authorized audio recordings of phone calls.-
Descrição: dc.descriptionFaculdade de Tecnologia (FT)-
Descrição: dc.descriptionDepartamento de Engenharia Elétrica (FT ENE)-
Descrição: dc.descriptionPrograma de Pós-Graduação em Engenharia Elétrica-
Idioma: dc.languageen-
Publicador: dc.publisherIEEE-
Relação: dc.relationhttps://ieeexplore.ieee.org/document/7775065-
Direitos: dc.rightsAcesso Restrito-
Palavras-chave: dc.subjectProcessamento de sinais acústicos-
Palavras-chave: dc.subjectGravações de áudio-
Palavras-chave: dc.subjectAnálise forense-
Palavras-chave: dc.subjectÁudio - adulteração-
Palavras-chave: dc.subjectFrequência da rede elétrica-
Título: dc.titleESPRIT-Hilbert-based audio tampering detection with SVM classifier for forensic analysis via electrical network frequency-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional – UNB

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