Abstract
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.
This research work is funded by the National Science Foundation of China (61071152, 60702043), 973 Program (2010CB731403, 2010CB731406) of China, Shanghai Educational Development Foundation and Project of Beijing Key Laboratory of Communication and Information System of Beijing Jiaotong University.
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Chen, L., Wang, S., Li, S., Li, J. (2012). New Feature Presentation of Transition Probability Matrix for Image Tampering Detection. In: Shi, Y.Q., Kim, HJ., Perez-Gonzalez, F. (eds) Digital Forensics and Watermarking. IWDW 2011. Lecture Notes in Computer Science, vol 7128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32205-1_30
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DOI: https://doi.org/10.1007/978-3-642-32205-1_30
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