A Comparative Study for Localization of Forgery Regions in Images

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dc.authorid0000-0002-0362-4017
dc.contributor.authorOzden, Mustafa
dc.contributor.authorSahin, Canberk
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractAs computer technologies and image processing software have advanced, it has become progressively easier to produce simple fake or forged images by altering digital images without leaving any discernible trace. There is a significant need to detect manipulated regions in images in crucial fields such as politics, law, and forensic medicine. In this study, we propose a method that combines the traditional techniques, such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), with the advantages of deep learning methods to detect manipulated regions in forged images. The proposed method involves designing an architecture where DWT and DCT are used in parallel with DenseNet based Convolutional Neural Network (CNN). To evaluate the effectiveness of this method, we implemented three alternative approaches: one that uses only DCT and CNN, another that uses only DWT and CNN, and a third that employs only CNN without either transformation. In total, four different methods were tested on eight datasets, and their performance was compared using metrics such as accuracy, precision, recall, dice similarity coefficient, and F1 score. The results from these comparisons clearly indicate the effectiveness and high classification accuracy of the proposed method. By leveraging the combined strengths of traditional image processing techniques and advanced deep learning algorithms, the proposed method demonstrates superior capability in detecting manipulated regions in forged images, thus offering a robust solution for applications in forensic field.
dc.identifier.doi10.1109/ACCESS.2025.3591571
dc.identifier.endpage130718
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105011620044
dc.identifier.scopusqualityQ1
dc.identifier.startpage130701
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3591571
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5899
dc.identifier.volume13
dc.identifier.wosWOS:001540960400024
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.subjectForgery
dc.subjectDiscrete wavelet transforms
dc.subjectDiscrete cosine transforms
dc.subjectFeature extraction
dc.subjectConvolutional neural networks
dc.subjectAccuracy
dc.subjectTransforms
dc.subjectFrequency-domain analysis
dc.subjectDeep learning
dc.subjectWatermarking
dc.subjectConvolutional neural networks (CNN)
dc.subjectdiscrete cosine transform (DCT)
dc.subjectdiscrete wavelet transform (DWT)
dc.subjectimage forgery detection
dc.titleA Comparative Study for Localization of Forgery Regions in Images
dc.typeArticle

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