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Image Splicing Detection Using Generalized Whittaker Function Descriptor

Dumitru Baleanu1,2,3, Ahmad Sami Al-Shamayleh4, Rabha W. Ibrahim5,*

1 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey
2 Institute of Space Sciences, Magurele-Bucharest, R76900, Romania
3 Department of Medical Research, China Medical University, 40402, Taiwan
4 Department of Networks & Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Al-Salt, Amman, 19328, Jordan
5 Near East University, Mathematics Research Center, Department of Mathematics, Near East Boulevard, PC: 99138, Nicosia /Mersin 10 – Turkey

* Corresponding Author: Rabha W. Ibrahim. Email: email

Computers, Materials & Continua 2023, 75(2), 3465-3477. https://doi.org/10.32604/cmc.2023.037162

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.

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APA Style
Baleanu, D., Al-Shamayleh, A.S., Ibrahim, R.W. (2023). Image splicing detection using generalized whittaker function descriptor. Computers, Materials & Continua, 75(2), 3465-3477. https://doi.org/10.32604/cmc.2023.037162
Vancouver Style
Baleanu D, Al-Shamayleh AS, Ibrahim RW. Image splicing detection using generalized whittaker function descriptor. Comput Mater Contin. 2023;75(2):3465-3477 https://doi.org/10.32604/cmc.2023.037162
IEEE Style
D. Baleanu, A.S. Al-Shamayleh, and R.W. Ibrahim "Image Splicing Detection Using Generalized Whittaker Function Descriptor," Comput. Mater. Contin., vol. 75, no. 2, pp. 3465-3477. 2023. https://doi.org/10.32604/cmc.2023.037162



cc 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.
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