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

Authors:
Guorong Xuan;Yun Q. Shi;Cong Huang;Dongdong Fu;Xiuming Zhu;Peiqi Chai;Jianjiong Gao
Affiliations:
Dept. of Computer Science, Tongji University, Shanghai, P.R. China;Dept. of Electrical & Computer Engineering, New Jersey Institute of Technology Newark, New Jersey;Dept. of Computer Science, Tongji University, Shanghai, P.R. China;Dept. of Electrical & Computer Engineering, New Jersey Institute of Technology Newark, New Jersey;Dept. of Computer Science, Tongji University, Shanghai, P.R. China;Dept. of Computer Science, Tongji University, Shanghai, P.R. China;Dept. of Computer Science, Tongji University, Shanghai, P.R. China
Venue:
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
Year:
2006

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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).