Copy-move forgery detection using multiresolution local binary patterns

Copy-move forgery is one of the most popular tampering artifacts in digital images. In this paper, we present an efficient method for copy-move forgery detection using Multiresolution Local Binary Patterns (MLBP). The proposed method is robust to geometric distortions and illumination variations of duplicated regions. Furthermore, the proposed block-based method recovers parameters of the geometric transformations. First, the image is divided into overlapping blocks and feature vectors for each block are extracted using LBP operators. The feature vectors are sorted based on lexicographical order. Duplicated image blocks are determined in the block matching step using k-d tree for more time reduction. Finally, in order to both determine the parameters of geometric transformations and remove the possible false matches, RANSAC (RANdom SAmple Consensus) algorithm is used. Experimental results show that the proposed approach is able to precisely detect duplicated regions even after distortions such as rotation, scaling, JPEG compression, blurring and noise adding.