An Improved Path-Based Transductive Support Vector Machines Algorithm for Blind Steganalysis Classification

Authors:
Xue Zhang;Shangping Zhong
Affiliations:
College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China 350108;College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China 350108 and Fujian Supercomputing Center, Fuzhou, China 350108
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

Citing 3
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Abstract

With the technologies of blind steganalysis becoming increasingly popular, a growing number of researchers concern in this domain. Supervised learning for classification is widely used, but this method is often time consuming and effort costing to obtain the labeled data. In this paper, an improved semi-supervised learning method: path-based transductive support vector machines (TSVM) algorithm with Mahalanobis distance is proposed for blind steganalysis classification, by using modified connectivity kernel matrix to improve the classification accuracy. Experimental results show that our proposed algorithm achieves the highest accuracy among all examined semi-supervised TSVM methods, especially for a small labeled data set.