Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA)
Document Type
Conference Proceeding
Publication Date
1-1-2006
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). © Springer-Verlag Berlin Heidelberg 2006.
Identifier
33845426373 (Scopus)
ISBN
[3540488251, 9783540488255]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/11922841_5
e-ISSN
16113349
ISSN
03029743
First Page
49
Last Page
60
Volume
4283 LNCS
Recommended Citation
Xuan, Guorong; Shi, Yun Q.; Huang, Cong; Fu, Dongdong; Zhu, Xiuming; Chai, Peiqi; and Gao, Jianjiong, "Steganalysis using high-dimensional features derived from co-occurrence matrix and class-wise non-principal components analysis (CNPCA)" (2006). Faculty Publications. 19192.
https://digitalcommons.njit.edu/fac_pubs/19192
