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Image Splicing Detection Based on Texture Features with Fractal Entropy

Razi J. Al-Azawi1, Nadia M. G. Al-Saidi2, Hamid A. Jalab3,*, Rabha W. Ibrahim4, Dumitru Baleanu5,6,7

1 Department of Laser and Optoelectronics Engineering, University of Technology, Baghdad, 10066, Iraq
2 Department of Applied Sciences, University of Technology, Baghdad, 10066, Iraq
3 Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
4IEEE: 94086547, Kuala Lumpur, 59200, Malaysia
5 Department of Mathematics, Cankaya University, Balgat, 06530, Ankara, Turkey
6 Institute of Space Sciences, R76900 Magurele-Bucharest, Romania
7 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan

* Corresponding Author: Hamid A. Jalab. Email: email

(This article belongs to the Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)

Computers, Materials & Continua 2021, 69(3), 3903-3915. https://doi.org/10.32604/cmc.2021.020368

Abstract

Over the past years, image manipulation tools have become widely accessible and easier to use, which made the issue of image tampering far more severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is transposed onto another image. The proposed study consists of four features extraction stages used to extract the important features from suspicious images, namely, Fractal Entropy (FrEp), local binary patterns (LBP), Skewness, and Kurtosis. The main advantage of FrEp is the ability to extract the texture information contained in the input image. Finally, the “support vector machine” (SVM) classification is used to classify images into either spliced or authentic. Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods. Overall, the proposed algorithm achieves an ideal balance between performance, accuracy, and efficacy, which makes it suitable for real-world applications.

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APA Style
Al-Azawi, R.J., Al-Saidi, N.M.G., Jalab, H.A., Ibrahim, R.W., Baleanu, D. (2021). Image splicing detection based on texture features with fractal entropy. Computers, Materials & Continua, 69(3), 3903-3915. https://doi.org/10.32604/cmc.2021.020368
Vancouver Style
Al-Azawi RJ, Al-Saidi NMG, Jalab HA, Ibrahim RW, Baleanu D. Image splicing detection based on texture features with fractal entropy. Comput Mater Contin. 2021;69(3):3903-3915 https://doi.org/10.32604/cmc.2021.020368
IEEE Style
R.J. Al-Azawi, N.M.G. Al-Saidi, H.A. Jalab, R.W. Ibrahim, and D. Baleanu "Image Splicing Detection Based on Texture Features with Fractal Entropy," Comput. Mater. Contin., vol. 69, no. 3, pp. 3903-3915. 2021. https://doi.org/10.32604/cmc.2021.020368

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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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