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Copy-Move Forgery Detection Based on Automatic Threshold Estimation
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Author(s): Aya Hegazi (Faculty of Computers and Informatics, Benha University, Benha, Egypt), Ahmed Taha (Faculty of Computers and Informatics, Benha University, Benha, Egypt)and Mazen Mohamed Selim (Faculty of Computers and Informatics, Benha University, Benha, Egypt)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 23
Source title:
International Journal of Sociotechnology and Knowledge Development (IJSKD)
Editor(s)-in-Chief: Lincoln Christopher Wood (University of Otago, New Zealand)and Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/IJSKD.2020010101
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Abstract
Recently, users and news followers across websites face many fabricated images. Moreover, it goes far beyond that to the point of defaming or imprisoning a person. Hence, image authentication has become a significant issue. One of the most common tampering techniques is copy-move. Keypoint-based methods are considered as an effective method for detecting copy-move forgeries. In such methods, the feature extraction process is followed by applying a clustering technique to group spatially close keypoints. Most clustering techniques highly depend on the existence of a specific threshold to terminate the clustering. Determination of the most suitable threshold requires a huge amount of experiments. In this article, a copy-move forgery detection method is proposed. The proposed method is based on automatic estimation of the clustering threshold. The cutoff threshold of hierarchical clustering is estimated automatically based on clustering evaluation measures. Experimental results tested on various datasets show that the proposed method outperforms other relevant state-of-the-art methods.
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