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).
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Xuan, G. et al. (2006). Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA). In: Shi, Y.Q., Jeon, B. (eds) Digital Watermarking. IWDW 2006. Lecture Notes in Computer Science, vol 4283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922841_5
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DOI: https://doi.org/10.1007/11922841_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48825-5
Online ISBN: 978-3-540-48827-9
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