Journal
DIGITAL INVESTIGATION
Volume 11, Issue 1, Pages 57-66Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.diin.2013.12.001
Keywords
Image steganography; Steganalysis; High-dimensional feature; Feature selection; Fisher criterion
Funding
- National Natural Science Foundation of China [61272489, 61379151, 61302159]
- National Postdoctoral Science Foundation of China [20110491838, 2012T50842]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDA06030601]
- Scientific and Technological Innovation Leading Talent of Zhengzhou [10LJRC182]
- Doctoral Dissertation Innovation Fund of Zhengzhou Information Science and Technology Institute [BSLWCX201203]
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A steganalytic feature selection method based on the Fisher criterion used in pattern recognition is proposed in this paper in order to reduce effectively the high dimensionality of the statistical features used in state-of-the-art steganalysis. First, the separability of each single-dimension feature in the feature space is evaluated using the Fisher criterion, and these features are reordered in descending order of separability. Then, starting from the first dimension of the reordered features, as the dimension increases, the separability of each feature component is analyzed using the Fisher criterion combined with the Euclidean distance. Finally, the feature components with the best separability are selected as the final steganalytic features. Experimental results based on the selection of SPAM (Subtractive Pixel Adjacency Matrix) features in spatial-domain steganalysis and CC-PEV (Cartesian Calibrated feature extracted by PEVny) features in DCT-domain steganalysis show that the proposed method can not only reduce the dimensionality of the features efficiently while maintaining the accuracy of the steganalysis, but also greatly improve the detection efficiency. (C) 2013 Elsevier Ltd. All rights reserved.
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