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Steganalysis based on distribution characters of stego-images in reduced dimension space

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Abstract

In this paper we propose an Improved Kernel Linear Discriminant Analysis algorithm to analyze the distribution differences between cover images and stego-images in the reduced dimensional space. We observe that the hidden information, the information hidden in the cover images, of stego-images are clustered in a plane while all other information of cover images are scattered more evenly in the whole space and have no other clusters. Based on this fact, we develop a steganalysis scheme to discriminate stego-images from innocent images. The experiment results show the effectiveness of the propose approach.

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Acknowledgements

This work is partially supported by the State Scholarship Fund (2007 104752), National Natural Science Foundation of China (61100170), Guangdong Province ordinary university science and technology innovation project(2012KJCX0079), Guangdong Modern Information Service Industry Develop Particularly item (No.13090), the Fundamental Research Funds for the Central Universities (12lgpy37).

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Authors and Affiliations

  1. Guangdong University of Education, Guangzhou, Guangdong, 510310, China

    Guoming Chen & Qiang Chen

  2. Sun Yat-sen University, Guangzhou, Guangdong, 510275, China

    Dong Zhang

  3. Eastern Washington University, Spokane, WA, 99202, USA

    Duanning Zhou

Authors
  1. Guoming Chen
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  2. Qiang Chen
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  3. Dong Zhang
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  4. Duanning Zhou
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Correspondence to Guoming Chen.

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Chen, G., Chen, Q., Zhang, D. et al. Steganalysis based on distribution characters of stego-images in reduced dimension space. Multimed Tools Appl 71, 497–515 (2014). https://doi.org/10.1007/s11042-013-1522-9

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  • DOI: https://doi.org/10.1007/s11042-013-1522-9

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