Quantization-based Markov feature extraction method for image splicing detection

In this paper, we propose an efficient Markov feature extraction method for image splicing detection using discrete cosine transform coefficient quantization. The quantization operation reduces the information loss caused by the coefficient thresholding used to restrict the number of Markov features. The splicing detection performance is improved because the quantization method enlarges the discrimination of the probability distributions between the authentic and the spliced images. In this paper, we present two Markov feature selection algorithms. After quantization operation, we choose the sum of three directional Markov transition probability values at the corresponding position in the probability matrix as a first feature vector. For the second feature vector, the maximum value among the three directional difference values of the three color channels is used. A fixed number of features, regardless of the color channels and test datasets, are used in the proposed algorithm. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. The average detection accuracy is over than 97% on three well-known splicing detection image datasets without the use of additional feature reduction algorithms. Furthermore, we achieve reasonable forgery detection performance for more modern and realistic dataset.