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Copy–move forgery detection in digital image forensics: A survey

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Abstract

The detection of copy-move forgeries has been of utmost relevance in the field of digital image forensics because of the explosive growth of image altering tools. The paper provides a thorough overview of current developments in copy-move forgery detection methods. Block-based, keypoints-based, and deep learning-based methods represent the three distinct categories into which the methodologies in the survey are divided. The papers in each category are thoroughly analysed, taking into consideration important factors including pre-processing techniques, feature extraction strategies, feature matching methods, and performance evaluation using various metrics and datasets. This survey study provides a thorough overview of the state of the field by methodically synthesizing and assessing the surveyed papers, and it also offers helpful insights for researchers and practitioners working to improve the accuracy and robustness of copy–move forgery detection methods in digital image forensics.

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  1. College of Computer Science and IT, University of Anbar, Ramadi, Iraq

    Mahmoud H. Farhan, Khalid Shaker & Sufyan Al-Janabi

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  1. Mahmoud H. Farhan
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  2. Khalid Shaker
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Farhan, M.H., Shaker, K. & Al-Janabi, S. Copy–move forgery detection in digital image forensics: A survey. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18399-2

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