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.
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Zhang, X., Zhong, S. (2009). An Improved Path-Based Transductive Support Vector Machines Algorithm for Blind Steganalysis Classification. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_50
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DOI: https://doi.org/10.1007/978-3-642-05253-8_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05252-1
Online ISBN: 978-3-642-05253-8
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