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Image Splicing Detection Based on Texture Features with Fractal Entropy

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dc.contributor.author Al-Azawi, Razi J.
dc.contributor.author Al-Saidi, Nadiam.G.
dc.contributor.author Jalab, Hamid A.
dc.contributor.author Ibrahim, Rabhaw
dc.contributor.author Baleanu, Dumitru
dc.date.accessioned 2022-05-23T12:27:24Z
dc.date.available 2022-05-23T12:27:24Z
dc.date.issued 2021
dc.identifier.citation Al-Azawi, Razi J...et al. (2021). "Image Splicing Detection Based on Texture Features with Fractal Entropy", Computers, Materials and Continua, Vol. 69, No. 3, pp. 3903-3915. tr_TR
dc.identifier.issn 1546-2218
dc.identifier.uri http://hdl.handle.net/20.500.12416/5546
dc.description.abstract Over the past years, image manipulation tools have become widely accessible and easier to use, whichmade the issue of image tampering farmore severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is transposed onto another image. The proposed study consists of four features extraction stages used to extract the important features from suspicious images, namely, Fractal Entropy (FrEp), local binary patterns (LBP), Skewness, and Kurtosis. The main advantage of FrEp is the ability to extract the texture information contained in the input image. Finally, the "support vector machine" (SVM) classification is used to classify images into either spliced or authentic. Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods. Overall, the proposed algorithm achieves an ideal balance between performance, accuracy, and efficacy, which makes it suitable for real-world applications. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2021 Tech Science Press. All rights reserved. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.32604/cmc.2021.020368 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Fractal Entropy tr_TR
dc.subject Image Splicing tr_TR
dc.subject Lbp tr_TR
dc.subject Svm tr_TR
dc.subject Texture Features tr_TR
dc.title Image Splicing Detection Based on Texture Features with Fractal Entropy tr_TR
dc.type article tr_TR
dc.relation.journal Computers, Materials and Continua tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 69 tr_TR
dc.identifier.issue 3 tr_TR
dc.identifier.startpage 3903 tr_TR
dc.identifier.endpage 3915 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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