JPEG steganalysis based on classwise non-principal components analysis and multi-directional Markov model

Document Type

Conference Proceeding

Publication Date

1-1-2007

Abstract

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 die existing steganalysis techniques in attacking modern JPEG steganographic schemes - F5, Outguess, MB1 and MB2. © 2007 IEEE.

Identifier

46449106462 (Scopus)

ISBN

[1424410177, 9781424410170]

Publication Title

Proceedings of the 2007 IEEE International Conference on Multimedia and Expo Icme 2007

External Full Text Location

https://doi.org/10.1109/icme.2007.4284797

First Page

903

Last Page

906

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