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03-07-2024 | Original Article

A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

Authors: Soumya Mukherjee, Arup Kumar Pal, Soham Maji

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2024

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Abstract

The article introduces a sophisticated copy-move forgery detection technique that leverages DBSCAN-based keypoint similarity matching. This method is particularly effective in identifying tampered areas within digital images, even when subjected to various post-processing attacks such as brightness changes, noise addition, and image blurring. The technique utilizes AKAZE keypoint detection, which is known for its speed and robustness against different post-processing and geometrical attacks. The proposed method demonstrates superior performance compared to conventional techniques, offering high precision and computational efficiency. The article also includes extensive experimental results and comparisons with existing methods, showcasing the effectiveness of the proposed technique across multiple benchmark datasets. The proposed method's robustness is further validated through rigorous testing against various post-processing attacks, highlighting its practical applicability in real-world scenarios.

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Metadata
Title
A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching
Authors
Soumya Mukherjee
Arup Kumar Pal
Soham Maji
Publication date
03-07-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02268-3