Region duplication detection in digital images based on Centroid Linkage Clustering of key–points and graph similarity matching

Abstract

Region duplication or copy–move forgery is an attack in which a region of an image is copied and pasted onto another location of the same image. In the recent state–of–the–art, a number of key–point based methods have been proposed for copy–move forgery detection in digital images. Though the problems of re–scaling and rotation in region duplication, have been sufficiently investigated using key–point based methods, post-processing based attacks such as flip, blur, brightness and noise, remain an open challenge in this field. In this paper, we address the problem of copy–move forgery detection in images, plus aim to identify copied regions, having undergone different geometric (such as rotation, re–scale), and post–processing attacks (such as Gaussian noise, blurring and brightness adjustment). In the proposed algorithm we introduce a region based key–point selection concept, which is considerably more discriminative than single SIFT key–point extraction. In this work, we apply Centroid Linkage Clustering, to identify duplicated regions in an image, from matched key-points. Also, we introduce a Graph Similarity Matching algorithm, to optimize false matches. Our experimental results demonstrate the efficiency of the proposed method in terms of forgery detection and localization efficiency, for a wide range of geometric and post-processing based attacks in region duplication.