Copy-Move Detection Based on Different Forms of Local Binary Patterns

Andrey Kuznetsov

Abstract: An obvious way of digital image forgery is a copy-move attack. It is quite simple to carry out to hide important information in an image. Copy-move process contains three main steps: copy the fragment from one place of an image, transform it by some means and paste to another place of the same image. Nowadays researchers develop a lot of copy-move detection solutions though the achieved results are far from perfect. In this paper, it is proposed a comparison of different local binary patterns (LBP) forms in the task of copy-move detection: geometric local binary patterns (GLBP), binary gradient contours (BGC), local derivative patterns (LDP) and simple LBP forms. All these LBP-based solutions are used to create local features that are robust to contrast enhancement, additive Gaussian noise, JPEG compression, affine transform. All these solutions are different in the number of transforms and transform parameters range that can be detected by the algorithm. Another advantage of these features is low computational complexity. Conducted experiments show that GLBP-based features can be used to detect all 4 transforms with a wide range of transforms parameters. The proposed solution showed high precision and recall values during experimental research for wide ranges of transform parameters. Thus, it showed a meaningful improvement in detection accuracy.