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author:

Zhang, Xue (Zhang, Xue.) [1] | Zhong, ShangPing (Zhong, ShangPing.) [2] (Scholars:钟尚平)

Indexed by:

EI

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. © 2009 Springer-Verlag.

Keyword:

Intelligent computing Labeled data Learning systems Semi-supervised learning Steganography Support vector machines

Community:

  • [ 1 ] [Zhang, Xue]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China
  • [ 2 ] [Zhong, ShangPing]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Zhong, ShangPing]Fujian Supercomputing Center, Fuzhou, 350108, China

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Source :

ISSN: 0302-9743

Year: 2009

Volume: 5855 LNAI

Page: 453-462

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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