Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA)

Guorong Xuan, Yun Q. Shi, Cong Huang, Dongdong Fu, Xiuming Zhu, Peiqi Chai, Jianjiong Gao

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

19 Scopus citations

Abstract

This paper presents a novel steganalysis scheme with high-dimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the commonly used steganographic schemes applied to spatial domain (Spread-Spectrum, LSB, QIM) or JPEG domain (OutGuess, F5, Model-Based).

Original languageEnglish (US)
Title of host publicationDigital Watermarking - 5th International Workshop, IWDW 2006, Proceedings
PublisherSpringer Verlag
Pages49-60
Number of pages12
ISBN (Print)3540488251, 9783540488255
DOIs
StatePublished - 2006
Event5th International Workshop on Digital Watermarking, IWDW 2006 - Jeju Island, Korea, Republic of
Duration: Nov 8 2006Nov 10 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4283 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Workshop on Digital Watermarking, IWDW 2006
CountryKorea, Republic of
CityJeju Island
Period11/8/0611/10/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Class-wise non-principal components analysis (CNPCA)
  • Co-occurrence matrix
  • Steganalysis

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