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Entropy 2019, 21(4), 371; https://doi.org/10.3390/e21040371

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

1
Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Faculty of Computer Systems and Software Engineering, University Malaysia Pahang, Kuantan 26300, Pahang, Malaysia
*
Author to whom correspondence should be addressed.
Received: 5 March 2019 / Revised: 31 March 2019 / Accepted: 2 April 2019 / Published: 5 April 2019
(This article belongs to the Special Issue The Fractional View of Complexity)
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Abstract

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors. View Full-Text
Keywords: image forgery; image splicing; fractional entropy; fractional calculus; discrete wavelet transform image forgery; image splicing; fractional entropy; fractional calculus; discrete wavelet transform
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Jalab, H.A.; Subramaniam, T.; Ibrahim, R.W.; Kahtan, H.; Noor, N.F.M. New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection. Entropy 2019, 21, 371.

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