Image splicing detection based on texture features with fractal entropy

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

Full text not available from this repository.


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.

Item Type: Article
Funders: Faculty Program Grant, University of Malaya, Malaysia (GPF096C-2020)
Uncontrolled Keywords: Fractal entropy; Image splicing; Texture features; LBP; SVM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 20 Jun 2022 03:05
Last Modified: 20 Jun 2022 03:05

Actions (login required)

View Item View Item