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

Guorong Xuan, Xia Cui, Yun Q. Shi, Wen Chen, Xuefeng Tong, Cong Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
PublisherIEEE Computer Society
Pages903-906
Number of pages4
ISBN (Print)1424410177, 9781424410170
DOIs
StatePublished - 2007
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: Jul 2 2007Jul 5 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Other

OtherIEEE International Conference onMultimedia and Expo, ICME 2007
CountryChina
CityBeijing
Period7/2/077/5/07

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Software

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