Digital image splicing detection based on Markov features in DCT and DWT domain

Image splicing is very common and fundamental in image tampering. To recover people's trust in digital images, the detection of image splicing is in great need. In this paper, a Markov based approach is proposed to detect this specific artifact. Firstly, the original Markov features generated from the transition probability matrices in DCT domain by Shi et al. is expanded to capture not only the intra-block but also the inter-block correlation between block DCT coefficients. Then, more features are constructed in DWT domain to characterize the three kinds of dependency among wavelet coefficients across positions, scales and orientations. After that, feature selection method SVM-RFE is used to fulfill the task of feature reduction, making the computational cost more manageable. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the final dimensionality-reduced feature vector. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

► An expanded Markov based approach is proposed for digital image splicing detection.
► Markov features are constructed not only in DCT domain but also in DWT domain.
► Feature selection method SVM-RFE is utilized for the task of feature reduction.
► Experimental results demonstrate that the approach outperforms the state of the art.