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A Priori knowledge based secure payload estimation

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Abstract

Many contemporary steganographic schemes aim to embed fixed-length secret message in the cover while minimizing the stego distortion. However, in some cases, the secret message sender requires to embed a variable-length secret payload within his expected stego security. This kind of problem is named as secure payload estimation (SPE). In this paper, we propose a practical SPE approach for individual cover. The stego security metric we adopt here is the detection error rate of steganalyzer (P E ). Our method is based on a priori knowledge functions, which are two kinds of functions to be determined before the estimation. The first function is the relation function of detection error rate and stego distortion (P E − D function). The second function reflects the relationship between stego distortion and payload rate (D − α) of the chosen cover. The P E − D is the general knowledge, which is calculated from image library. On the other hand, D − α is for specific cover, which is needed to be determined on site. The estimating procedure is as follows: firstly, the sender solves the distortion D under his expected P E via P E − D, and then calculates the corresponding secure payload α via D − α of the cover. For on-site operations, the most time-consuming part is calculating D − α function for cover image, which costs 1 time of STC coding. Besides this, the rest on-site operations are solving single-variable formulas, which can be easily tackled. Our approach is an efficient and practical solution for SPE problem.

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Acknowledgements

Special thank to Prof. Jessica Fridrich. She pointed out that model of the work proposed in this paper can be named as PELS. The authors appreciate the members of DDE Laboratory in SUNY Binghamton for sharing their codes on the site: dde.binghamton.edu and the anonymous reviewers for their constructive suggestions for this paper.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Sai Ma, Xianfeng Zhao & Qingxiao Guan

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100093, China

    Sai Ma, Xianfeng Zhao & Qingxiao Guan

  3. Beijing Information Technology Institute, Beijing, 100094, China

    Zhoujun Xu & Yi Ma

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  1. Sai Ma
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  2. Xianfeng Zhao
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  3. Qingxiao Guan
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  4. Zhoujun Xu
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  5. Yi Ma
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Corresponding author

Correspondence to Xianfeng Zhao.

Additional information

This work was supported by the NSFC under U1636102 and U1536105, National Key Technology R&D Program under 2014BAH41B01 and 2016YFB0801003, and Strategic Priority Research Program of CAS under XDA06030600.

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Ma, S., Zhao, X., Guan, Q. et al. A Priori knowledge based secure payload estimation. Multimed Tools Appl 77, 17889–17911 (2018). https://doi.org/10.1007/s11042-017-4955-8

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  • DOI: https://doi.org/10.1007/s11042-017-4955-8

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