An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

Author:

Hegazi Aya,Taha Ahmed,Selim Mazen M.

Publisher

Elsevier BV

Subject

General Computer Science

Reference36 articles.

1. 2-Levels of clustering strategy to detect and locate copy-move forgery in digital images;Abdel-Basset;Multimedia Tools Appl.,2018

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3. Detection of copy-move image forgery based on discrete cosine transform;Alkawaz;Neural Comput. Appl.,2018

4. A SIFT-based forensic method for copy–move attack detection and transformation recovery;Amerini;IEEE Trans. Inform. Forensic Secur.,2011

5. Copy-move forgery detection and localization by means of robust clustering with J-linkage;Amerini;Signal Process. Image Commun.,2013

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1. Comparative Analysis of Deep Learning Approaches for Detecting Copy-Move Forgery;2024 International Conference on Computational Intelligence and Computing Applications (ICCICA);2024-05-23

2. SAM-based Localization Method for Image Copy-Move Forgery;Proceedings of the 2024 10th International Conference on Computer Technology Applications;2024-05-15

3. CAMU-Net: Copy-move forgery detection utilizing coordinate attention and multi-scale feature fusion-based up-sampling;Expert Systems with Applications;2024-03

4. Copy-Move Video Forgery Detection Techniques: A Systematic Survey with Comparisons, Challenges and Future Directions;Wireless Personal Communications;2024-02

5. The Optimal Model for Copy-Move Forgery Detection in Medical Images;Journal of Medical Signals & Sensors;2024-02

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