Copy-move forgery detection and localization by means of robust clustering with J-Linkage

By Irene Amerini, Lamberto Ballan, Roberto Caldelli, A. del Bimbo, Luca Del Tongo, Giuseppe Serra. Signal processing. Image communication, 2013

299 citations recorded by the source corpus.

Understanding if a digital image is authentic or not, is a key purpose of image forensics. There are several different tampering attacks but, surely, one of the most common and immediate one is copy-move. A recent and effective approach for detecting copy-move forgeries is to use local visual features such as SIFT. In this kind of methods, SIFT matching is often followed by a clustering procedure to group keypoints that are spatially close. Often, this procedure could be unsatisfactory, in particular in those cases in which the copied patch contains pixels that are spatially very distant among them, and when the pasted area is near to the original source. In such cases, a better estimation of the cloned area is necessary in order to obtain an accurate forgery localization. In this paper a novel approach is presented for copy-move forgery detection and localization based on the JLinkage algorithm, which performs a robust clustering in the space of the geometric transformation. Experimental results, carried out on different datasets, show that the proposed method outperforms other similar state-of-the-art techniques both in terms of copy-move forgery detection reliability and of precision in the manipulated patch localization.

View Paper: https://www.semanticscholar.org/paper/b4b7dc93d913556651314e00ad0aca16ab8a2d2b

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Citations in the corpus

• Efficient Dense-Field Copy–Move Forgery Detection (2015, methodology): “More recent variants of this method take special care of multiple duplicated regions by hierarchical agglomerative clustering [6] or the J-linkage algorithm [7].”
• Segmentation-Based Image Copy-Move Forgery Detection Scheme (2015, methodology): “The second database MICC-F600 was introduced by Amerini et al [28].”