Image Manipulation Detection Using Residual and Dense Connection-based Deep Learning Models

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dc.contributor.authorKarakiş, Rukiye
dc.contributor.authorGürkahraman, Kali
dc.contributor.authorÜnsal, Emre
dc.date.accessioned2024-10-26T17:51:08Z
dc.date.available2024-10-26T17:51:08Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University
dc.description32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235
dc.description.abstractDigital image forgery poses a security threat by manipulating personal data shared on open networks in recent years. In this study, the performance of deep learning models with residual or dense connections, with or without transfer learning, for the classification problem of multiple image manipulations with dual JPEG compression, has been investigated. Transfer learning has produced higher and more stable results at different compression rates. The average accuracy values of the proposed model with dense connections were obtained as 0.9920, 0.9953, and 0.9907 for compression quality factors of 75, 85, and 95, respectively. These values were higher compared to similar studies in the literature. © 2024 IEEE.
dc.identifier.doi10.1109/SIU61531.2024.10601141
dc.identifier.isbn979-835038896-1
dc.identifier.scopus2-s2.0-85200850386
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10601141
dc.identifier.urihttps://hdl.handle.net/20.500.12418/26047
dc.identifier.wosWOS:001297894700327
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectdeep learning; digital image forensics; dual JPEG compression; image forgery; manipulation detection
dc.titleImage Manipulation Detection Using Residual and Dense Connection-based Deep Learning Models
dc.title.alternativeArtık ve Yoğun Bağlantı Tabanlı Derin Öğrenme Modelleri Kullanarak Görüntü Manipülasyonu Tespiti
dc.typeConference Object

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