Image splicing detection based on noncausal Markov model

In this paper, a noncausal Markov model is proposed for digital image splicing detection. Different from the traditional Markov model in image splicing detection, the proposed approach models an observation array as a 2-D noncausal signal and captures the underlying statistical characteristics. We give the solutions to the model and the model parameters are treated as discriminative features for classification (detection). To evaluate the generalization and effectiveness of the proposed method, we apply the model in the block DCT domain and discrete Meyer wavelet transform domain respectively and experimental results have shown that the proposed approach outperforms most of the state-of-the-art methods.