Forensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks

dc.contributor.authorNofan, Mohammed Waleed
dc.contributor.authorAta, Oǧuz
dc.date.accessioned2025-08-14T14:02:26Z
dc.date.available2025-08-14T14:02:26Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.descriptionVolume editors : Wibowo F.W. Conference name : 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation, ICoCSETI 2025. Conference city : Jakarta. Conference date : 21 January 2025. Conference code : 209376.
dc.description.abstractNowadays, there is a wide use of modern techniques and technology in general, which constitutes an important innovation. Manipulating, changing and modifying digital images has become very popular and relatively easy, which has created a great challenge and concern, especially in cases that rely on digital images as the main evidence for issuing judgments. In order to improve the quality of images and accurately identify fraud cases, a talented system has been invented to conduct forensic investigations by combining modern image processing techniques and Convolutional Neural Networks (CNN). In this experiment, Google Colab was used as an experimental platform to apply a pixel-based approach to extract features from three separate experiments. An approach to forgery detection using pixel base algorithm was tested in relation to multiple conditions within separate experiments involving CNNs integration. Experiment1, which had a rudimentary CNN architecture, achieved an accuracy of 94.53% and validation accuracy of 94.54%. Experiment 2, with a sophisticated CNN structure, resulted in an accuracy of 94.91%, and a validation accuracy of 94.92%.
dc.identifier.citationNofan, M. W., & Ata, O. (2025, January). Forensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks. In 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), 548-553. IEEE. 10.1109/ICoCSETI63724.2025.11020021
dc.identifier.doi10.1109/ICoCSETI63724.2025.11020021
dc.identifier.endpage553
dc.identifier.isbn9798331508616
dc.identifier.scopus2-s2.0-105010138717
dc.identifier.startpage548
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5846
dc.indekslendigikaynakScopus
dc.institutionauthorAta, Oǧuz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofICoCSETI 2025 - International Conference on Computer Sciences, Engineering, and Technology Innovation, Proceeding
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectForensic image analysis
dc.subjectForgery detection
dc.subjectimage processing
dc.titleForensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks
dc.typeConference Object

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