Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 25, Issue 2, Pages 185-199Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2014.2347513
Keywords
2-D noncausal Markov model; block discrete cosine transformation (BDCT); discrete Meyer wavelet transform; passive image-splicing detection
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Funding
- National Science Foundation of China [61271316, 61071152, 61271319, 61271180]
- 973 Programs of China [2010CB731403, 2010CB731406, 2013CB329605]
- National Twelfth Five-Year Plan for Science and Technology [2012BAH38B04]
- Key Laboratory for Shanghai Integrated Information Security Management Technology Research
- Chinese National Engineering Laboratory for Information Content Analysis Technology
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In this paper, a 2-D noncausal Markov model is proposed for passive digital image-splicing detection. Different from the traditional Markov model, the proposed approach models an image as a 2-D noncausal signal and captures the underlying dependencies between the current node and its neighbors. The model parameters are treated as the discriminative features to differentiate the spliced images from the natural ones. We apply the model in the block discrete cosine transformation domain and the discrete Meyer wavelet transform domain, and the cross-domain features are treated as the final discriminative features for classification. The support vector machine which is the most popular classifier used in the image-splicing detection is exploited in our paper for classification. To evaluate the performance of the proposed method, all the experiments are conducted on public image-splicing detection evaluation data sets, and the experimental results have shown that the proposed approach outperforms some state-of-the-art methods.
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