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Image Splicing Detection Using Generalized Whittaker Function Descriptor

dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorAl-Shamayleh, Ahmad Sami
dc.contributor.authorIbrahim, Rabha W.
dc.contributor.authorID56389tr_TR
dc.date.accessioned2023-12-29T13:42:01Z
dc.date.available2023-12-29T13:42:01Z
dc.date.issued2023
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractImage 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.en_US
dc.identifier.citationbALEANU, d.;...ET.AL. (2023). "Image Splicing Detection Using Generalized Whittaker Function Descriptor", Computers, Materials and Continua, vOL.75, nO.2, PP.3465-3477.en_US
dc.identifier.doi10.32604/cmc.2023.037162
dc.identifier.endpage3477en_US
dc.identifier.issn15462218
dc.identifier.issue2en_US
dc.identifier.startpage3465en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/6826
dc.identifier.volume75en_US
dc.language.isoenen_US
dc.relation.ispartofComputers, Materials and Continuaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFractional Calculusen_US
dc.subjectımage Authenticationen_US
dc.subjectImage Forgeryen_US
dc.subjectK-Symbolen_US
dc.subjectSVMen_US
dc.subjectTexture Featuresen_US
dc.subjectWhittaker Functionen_US
dc.titleImage Splicing Detection Using Generalized Whittaker Function Descriptortr_TR
dc.titleImage Splicing Detection Using Generalized Whittaker Function Descriptoren_US
dc.typeArticleen_US
dspace.entity.typePublication

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