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JPEG Steganalysis Based on Classwise Non-Principal Components Analysis and Multi-Directional Markov Model

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6 Author(s)

This paper presents a new steganalysis scheme to attack JPEG steganography. The 360 dimensional feature vectors sensitive to data embedding process are derived from multidirectional Markov models in the JPEG coefficients domain. The class-wise non-principal components analysis (CNPCA) is proposed to classify steganograpghy in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking modern JPEG steganographic schemes-F5, Outguess, MB1 and MB2.

Published in:

Multimedia and Expo, 2007 IEEE International Conference on

Date of Conference:

2-5 July 2007