A novel pixel's fractional mean-based image enhancement algorithm for better image splicing detection

Jalab, Hamid A. and Alqarni, Mohammed A. and Ibrahim, Rabha W. and Almazroi, Abdulwahab Ali (2022) A novel pixel's fractional mean-based image enhancement algorithm for better image splicing detection. Journal of King Saud University Science, 34 (2). ISSN 1018-3647, DOI https://doi.org/10.1016/j.jksus.2021.101805.

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Image manipulation has become widely accessible to the masses over the past years due to the sophisticated image editing tools which are readily-available and easy to use. As a result, image forgery has increased such that it has become infeasible to discriminate authentic from tampered images with the naked eye. Image forgery plays a prominent role in the spread of misinformation, which might be criminalized under certain jurisdictions. Image splicing is a common type of image manipulation and constitutes one of the most widespread image tampering methods on the internet. Efforts have been made to tackle the implications of image forgery by developing computer algorithms that can discriminate tampered images, however, more research is needed to keep up with the advancements of image editing tools. Previously, we have explored fractional calculus in other image processing applications. In this study, we propose a novel pixel's fractional mean (PFM) algorithm to enhance images prior to classification for better detection of image splicing forgery based on texture features. The proposed PFM enhances each pixel separately depending on the occurrence number of the pixel's intensity. Two sets of texture algorithms are used to extract essential features from suspected spliced images. These features are then used with the ``support vector machine'' (SVM) classifier to classify authentic and spliced images. The proposed model demonstrated an accuracy rate of 97% when evaluated with the publicly-available image splicing dataset ``CASIA v2.0''. With a relatively low dimension feature vector, the proposed model demonstrates high accuracy and efficiency, which corroborate the benefit of using fractional calculus in image processing algorithms. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Item Type: Article
Funders: Universiti Malaya [Grant No: GPF096C-2020]
Uncontrolled Keywords: Image splicing; Fractional differential; Texture features; Support vector machine
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 Aug 2022 03:18
Last Modified: 03 Aug 2022 03:18
URI: http://eprints.um.edu.my/id/eprint/33414

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