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

Guorong Xuan, Yun-Qing Shi, Cong Huang, Dongdong Fu, Peiqi Chai

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
StatePublished - Jan 1 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

Other

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

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

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

Cite this

Xuan, G., Shi, Y-Q., Huang, C., Fu, D., & Chai, P. (2006). Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA). In Digital Watermarking - 5th International Workshop, IWDW 2006, Proceedings (pp. 49-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4283 LNCS). Springer Verlag.