Universal Steganalysis Using Multiwavelet Higher-Order Statistics and Support Vector Machines

Authors:
San-Ping Li;Yu-Sen Zhang;Chun-Hua Li;Feng Zhao
Affiliations:
Institute of Command Automation, PLA University of Sci. and Tech, Nanjing 210007, China;Institute of Command Automation, PLA University of Sci. and Tech, Nanjing 210007, China;College of Computer Sci. and Tech., Huazhong University of Sci. and Tech., Wuhan, 430074, China;Institute of Command Automation, PLA University of Sci. and Tech, Nanjing 210007, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

In this paper, a new universal steganalysis algorithm based on multiwavelet higher-order statistics and Support Vector Machines(SVM) is proposed. We follow the philosophy introduced in Ref[7] in which the features are calculated from the stego image's noise component in the wavelet domain. Instead of working in wavelet domain, we calculate the features in multiwavelet domain. We call this Multiwavelet Higher-Order Statistics (MHOS) feature. A nonlinear SVM classifier is then trained on a database of images to construct a universal steganalyzer. The comparison to the current state-of-the-art universal steganalyzers, which was performed on the same image databases under the same testing conditions, indicates that the proposed universal steganalysis offers improved performance.