Open Access

ARTICLE

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

Zaid Nidhal Khudhair^1,4, Farhan Mohamed², Amjad Rehman^3,*, Tanzila Saba³, Saeed Ali bahaj³

1 Faculty of Engineering, School of Computing, University Technology of Malaysia, Johar Bahru, Malaysia
2 UTM-IRDA MaGICX, Institute of Human Centered Engineering, Universiti Teknologi Malaysia, Malaysia
3 Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh, Saudi Arabia
4 Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

* Corresponding Author: Amjad Rehman. Email:

Computers, Materials & Continua 2023, 74(2), 4135-4147. https://doi.org/10.32604/cmc.2023.032315

Received 13 May 2022; Accepted 18 August 2022; Issue published 31 October 2022

Abstract

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition (SVD). It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size. At each step, the SVD is determined. First, the diagonal matrix’s maximum value (norm) is selected (representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating the matrix or scaled). Then, the similar norms are grouped, and each leading group is separated into many subgroups (elements of each subgroup are neighbors) according to 8-adjacency (the subgroups for each leading group must be far from others by a specific distance). After that, a weight is assigned for each subgroup to classify the image as forgery or not. Finally, the F1 score of the proposed system is measured, reaching 99.1%. This approach is robust against rotation, scaling, noisy images, and illumination variation. It is compared with other similar methods and presents very promised results.

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Keywords

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