Copy-Move Detection Using Gray Level Run Length Matrix Features

Abstract

Copy-move detection is a well-recognized and active area of research owing to great demand of authenticating genuineness of images. Currently, available techniques for copy-move detection fail to accurately locate the tampered region and lack robustness against common post-processing operations like compression, blurring, and brightness changes. This paper proposes a novel technique for the detection and localization of copy-move regions in image using gray level run length matrix (GLRLM) features. In the proposed method, we first divide the forged image into overlapping blocks and GLRLM features are calculated for each block. Features calculated for each block form feature vectors. Feature vectors thus obtained are lexicographically sorted. Blocks with similar features are identified using Euclidean feature distances. Post-processing isolates similar blocks. Results demonstrate the effectiveness of the proposed scheme to locate copy-move forgery and robustness against operations like JPEG compression, blurring, and contrast adjustments.