Splicing image forgery detection based on DCT and Local Binary Pattern

The authenticity of a digital image suffers from severe threats due to the rise of powerful digital image editing tools that easily alter the image contents without leaving any visible traces of such changes. In this paper, a novel passive splicing image forgery detection scheme based on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is proposed. First, the chrominance component of the input image is divided into overlapping blocks. Then, for each block, LBP is calculated and transformed into frequency domain using 2D DCT. Finally, standard deviations are calculated of respective frequency coefficients of all blocks and they are used as features. For classification, a support vector machine (SVM) is used. Experimental results on benchmark splicing image forgery databases show that the detection accuracy of the proposed method is up to 97%, which is the best accuracy so far.