New Feature Presentation of Transition Probability Matrix for Image Tampering Detection
Extraction of discriminative feature is crucial to machine learning approach of image tampering detection. The state-of-the-art Markov transition probability feature is extended in this paper. We show that correlation between adjacent elements on the difference array of block DCT coefficients can be theoretically calculated and provides little information to the classification problem. We propose to decorrelate the variables and use the marginal distribution as feature in image tampering detection. The framework is applied to 1st and 2nd order Markov transition probability feature. Our experiment result shows the new presentation of the feature has competitive performance and greatly reduced dimensionality.KeywordsImage Tampering DetectionImage Splicing DetectionMarkov Transition ProbabilityMachine LearningPattern Recognition