KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Detection for JPEG steganography based on evolutionary feature selection and classifier ensemble selection

  • Ma, Xiaofeng (State key Laboratory of Mathematical Engineering and Advanced Computing) ;
  • Zhang, Yi (State key Laboratory of Mathematical Engineering and Advanced Computing) ;
  • Song, Xiangfeng (Xi'an Communication Institute) ;
  • Fan, Chao (Zhengzhou Science and Technology Institute)
  • Received : 2017.05.04
  • Accepted : 2017.08.01
  • Published : 2017.11.30
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Abstract

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

Keywords

References

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