A Steganalysis Based on OC-SVM and Two Clustering Methods

Hai Bin Yang; Dun Tao Guo; Peng Luo

doi:10.4028/www.scientific.net/AMM.263-266.3096

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266A Steganalysis Based on OC-SVM and Two Clustering...

A Steganalysis Based on OC-SVM and Two Clustering Methods

Abstract:

In this paper, a novel and efficient steganalysis based on improved K-means clustering using One-Class Support Vector Machine (OC-SVM) was proposed to blindly determine the existence of hidden messages in an image. The performance of sample clustering is concerned in the OC-SVM with multi-sphere. In previous work, the K-means was mainly used to create such multi-sphere by clustering. But the traditional K-means depends on initial clustering centers and ended local minimum value. So, to improve the clustering performance of K-means, the SA is employed into the scheme, which can create more reasonable multi-sphere by finding global optimum solutions in the clustering process. Simulation results with the chosen feature set and well-known steganographic techniques indicated that our approach was able to afford reasonable accuracy to distinguish between covers and stego images.

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Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

3096-3099

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.3096

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Online since:

December 2012

Authors:

Hai Bin Yang, Dun Tao Guo, Peng Luo

Keywords:

K-Mean, One-Class Support Vector Machine, Steganalysis, Stimulated Annealing

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