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Open AccessArticle

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

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Institute of Mathematical Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Author to whom correspondence should be addressed.
Academic Editors: J. A. Tenreiro Machado and António M. Lopes
Entropy 2015, 17(7), 4775-4785; https://doi.org/10.3390/e17074775
Received: 19 May 2015 / Revised: 2 July 2015 / Accepted: 3 July 2015 / Published: 8 July 2015
(This article belongs to the Special Issue Complex and Fractional Dynamics)
Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people’s trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional mask weight window on eight directions. Consequently, the fractional differential texture descriptors are extracted using the gray-level co-occurrence matrix for image splicing detection. The support vector machine is used as a classifier that distinguishes between authentic and spliced images. Results prove that the achieved improvements of the proposed algorithm are compatible with other splicing detection methods. View Full-Text
Keywords: fractional differential; Machado entropy; image splicing; feature extraction; dimension reduction; support vector machine fractional differential; Machado entropy; image splicing; feature extraction; dimension reduction; support vector machine
MDPI and ACS Style

Ibrahim, R.W.; Moghaddasi, Z.; Jalab, H.A.; Noor, R.M. Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection. Entropy 2015, 17, 4775-4785.

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