Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

  • Authors:

  • Qingzhong Liu;Andrew H. Sung;Zhongxue Chen;Jianyun Xu

  • Affiliations:

  • Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA;Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA and Institute for Complex Additive Systems Analysis, New Mexico Institute of Mining and Techno ...;Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA;Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

In this paper, we present a scheme based on feature mining and pattern classification to detect LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Five types of features are proposed. In comparison with other well-known feature sets, the set of proposed features performs the best. We compare different learning classifiers and deal with the issue of feature selection that is rarely mentioned in steganalysis. In our experiments, the combination of a dynamic evolving neural fuzzy inference system (DENFIS) with a feature selection of support vector machine recursive feature elimination (SVMRFE) achieves the best detection performance. Results also show that image complexity is an important reference to evaluation of steganalysis performance.