Citation

Jiao, Li Xian and Ng, Kok Why and Tong, Hau Lee (2026) Image copy-move forgery detection: a survey of methods, datasets, and emerging trends. Bulletin of Electrical Engineering and Informatics, 15 (4). pp. 3080-3094. ISSN 2089-3191

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Abstract

Digital image forgery has become a critical concern in the era of advanced multimedia technologies, where the authenticity of visual content directly affects trust in digital communication, journalism, and law enforcement. Among various forgery techniques, copy-move forgery (CMF) is among the most common and deceptive, as it involves duplicating a region of an image to conceal or misrepresent information. To address this challenge, numerous copy-move forgery detection (CMFD) approaches have been proposed, ranging from block-based and keypoint-based methods to hybrid models and deep learning (DL) techniques. This paper provides a comprehensive review of these approaches, analyzing their strengths and limitations, and evaluating their performance across multiple benchmark datasets. The evaluation considers factors such as image resolution, manipulation types, and robustness against post-processing attacks. By systematically comparing the algorithms and datasets, the study highlights persistent challenges and outlines future research directions. The findings aim to guide researchers in selecting appropriate techniques and inspire the development of more robust CMFD solutions.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Digital image forensics, Keypoint-based
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 03:25
Last Modified:	04 Sep 2026 03:25
URII:	http://shdl.mmu.edu.my/id/eprint/16698

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