Copy-Move forgery detection in images using grey-level run length matrix features

Abstract

Copy-move detection also known as region duplication detection is a well-recognised 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 detection and localisation of copy-move regions in image using grey-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. The results demonstrate the effectiveness of the proposed scheme to locate copy-move forgery and robustness against operations like jpeg compression, blurring and contrast adjustments.