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JPEG Steganalysis With High-Dimensional Features and Bayesian Ensemble Classifier

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4 Author(s)
Fengyong Li ; School of Communication, Shanghai University, Shanghai, China ; Xinpeng Zhang ; Bin Chen ; Guorui Feng

This work proposes a JPEG steganalytic scheme based on high-dimensional features and Bayesian ensemble classifier. The proposed scheme employs 15700 dimension features calculated from the co-occurrence matrices of DCT coefficients and coefficient differences, which indicate the intra-block and inter-block dependencies of image content. Furthermore, a number of sub-classifiers trained on the features are integrated as an ensemble classifier with a Bayesian mechanism, which is used to give optimal decisions for suspicious images. Experimental results show that both the high-dimensional features and the Bayesian mechanism contribute to the extended scheme, and the performance of the extended scheme is better than those of previous schemes.

Published in:

IEEE Signal Processing Letters  (Volume:20 ,  Issue: 3 )