Image Splicing Detection Based on the Q-Markov Features

Abstract

Recently, image splicing tamper detection has become an increasingly significant challenge, because of which all color information of color images and low detection rate of existing algorithms cannot be exploited. To overcome the shortcomings of this method, we propose a model, which employs difference matrix in quaternion domain (Q-DIFF) and Markov in quaternion domain (Q-Markov) in the quaternion discrete cosine transform domain (QDCT) for encoding tampering traces and quaternion back propagation neural network (QBPNN) for decision making. Furthermore, by introducing Q-DIFF and Q-Markov in the proposed model, the entire architecture of the algorithm is accumulated in the four-dimensional frequency domain (i.e., all color channels of color images are utilized). Moreover, the experimental results on public domain benchmark datasets demonstrate that the proposed model is superior to the other state-of-the-art splicing detection methods. Based on the experimental results, we suggest the direction that designs image tamper detection model, which invite all the processing in the model to operate in four-dimensional space (i.e. quaternion space).