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Image splicing detection based on texture features with fractal entropy

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 n...

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Main Authors:	Al-Azawi, Razi J., Al-Saidi, Nadia M. G., Jalab, Hamid A., Ibrahim, Rabha W., Baleanu, Dumitru
Format:	Article
Published:	Tech Science Press 2021
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/27951/
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spelling	my.um.eprints.279512022-06-20T03:05:08Z http://eprints.um.edu.my/27951/ Image splicing detection based on texture features with fractal entropy Al-Azawi, Razi J. Al-Saidi, Nadia M. G. Jalab, Hamid A. Ibrahim, Rabha W. Baleanu, Dumitru QA75 Electronic computers. Computer science 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. Tech Science Press 2021 Article PeerReviewed Al-Azawi, Razi J. and Al-Saidi, Nadia M. G. and Jalab, Hamid A. and Ibrahim, Rabha W. and Baleanu, Dumitru (2021) Image splicing detection based on texture features with fractal entropy. CMC-Computers Materials & Continua, 69 (3). pp. 3903-3915. ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2021.020368 <https://doi.org/10.32604/cmc.2021.020368>. 10.32604/cmc.2021.020368
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Azawi, Razi J. Al-Saidi, Nadia M. G. Jalab, Hamid A. Ibrahim, Rabha W. Baleanu, Dumitru Image splicing detection based on texture features with fractal entropy
description	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.
format	Article
author	Al-Azawi, Razi J. Al-Saidi, Nadia M. G. Jalab, Hamid A. Ibrahim, Rabha W. Baleanu, Dumitru
author_facet	Al-Azawi, Razi J. Al-Saidi, Nadia M. G. Jalab, Hamid A. Ibrahim, Rabha W. Baleanu, Dumitru
author_sort	Al-Azawi, Razi J.
title	Image splicing detection based on texture features with fractal entropy
title_short	Image splicing detection based on texture features with fractal entropy
title_full	Image splicing detection based on texture features with fractal entropy
title_fullStr	Image splicing detection based on texture features with fractal entropy
title_full_unstemmed	Image splicing detection based on texture features with fractal entropy
title_sort	image splicing detection based on texture features with fractal entropy
publisher	Tech Science Press
publishDate	2021
url	http://eprints.um.edu.my/27951/
_version_	1738510680863014912
score	13.211869

Image splicing detection based on texture features with fractal entropy

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