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Image Splicing Detection Using Generalized Whittaker Function Descriptor

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dc.contributor.author Baleanu, Dumitru
dc.contributor.author Al-Shamayleh, Ahmad Sami
dc.contributor.author Ibrahim, Rabha W.
dc.date.accessioned 2023-12-29T13:42:01Z
dc.date.available 2023-12-29T13:42:01Z
dc.date.issued 2023
dc.identifier.citation bALEANU, d.;...ET.AL. (2023). "Image Splicing Detection Using Generalized Whittaker Function Descriptor", Computers, Materials and Continua, vOL.75, nO.2, PP.3465-3477. tr_TR
dc.identifier.issn 15462218
dc.identifier.uri http://hdl.handle.net/20.500.12416/6826
dc.description.abstract Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the “support vector machine” (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel. When tested on publicly available image dataset “CASIA” v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.32604/cmc.2023.037162 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Fractional Calculus tr_TR
dc.subject ımage Authentication tr_TR
dc.subject Image Forgery tr_TR
dc.subject K-Symbol tr_TR
dc.subject SVM tr_TR
dc.subject Texture Features tr_TR
dc.subject Whittaker Function tr_TR
dc.title Image Splicing Detection Using Generalized Whittaker Function Descriptor 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 75 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 3465 tr_TR
dc.identifier.endpage 3477 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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