Forensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks
dc.contributor.author | Nofan, Mohammed Waleed | |
dc.contributor.author | Ata, Oǧuz | |
dc.date.accessioned | 2025-08-14T14:02:26Z | |
dc.date.available | 2025-08-14T14:02:26Z | |
dc.date.issued | 2025 | |
dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | |
dc.description | Volume 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.abstract | Nowadays, 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.citation | Nofan, 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.doi | 10.1109/ICoCSETI63724.2025.11020021 | |
dc.identifier.endpage | 553 | |
dc.identifier.isbn | 9798331508616 | |
dc.identifier.scopus | 2-s2.0-105010138717 | |
dc.identifier.startpage | 548 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12939/5846 | |
dc.indekslendigikaynak | Scopus | |
dc.institutionauthor | Ata, Oǧuz | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | ICoCSETI 2025 - International Conference on Computer Sciences, Engineering, and Technology Innovation, Proceeding | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Öğrenci | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Convolutional Neural Networks | |
dc.subject | Deep Learning | |
dc.subject | Forensic image analysis | |
dc.subject | Forgery detection | |
dc.subject | image processing | |
dc.title | Forensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks | |
dc.type | Conference Object |
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