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Journal of Real-Time Image Processing

, Volume 16, Issue 3, pp 649–660 | Cite as

Efficient stego key recovery based on distribution differences of extracting message bits

  • Jiufen Liu
  • Junjun Gan
  • Junchao Wang
  • Che Xu
  • Xiangyang LuoEmail author
Special Issue Paper
  • 46 Downloads

Abstract

The extraction of embedded messages, i.e., extraction attacks are the ultimate purpose of steganalysis, with great practical significance to obtain covert communication content and covert communication forensics. For steganography using a stego key, the extraction attacks are equivalent to the stego key recovery. This paper mainly studies the methods of efficient stego key recovery for LSB steganography in JPEG domain. According to the distribution differences of extracting message bits generated by the correct and incorrect keys, stego key recovery is transformed into the hypothesis test of the message bits distribution extracted by the correct and incorrect keys. Firstly, by fitting the message bits distribution extracted by the correct key, a stego key recovery method based on nonparametric hypothesis test is proposed. Secondly, by utilizing the distribution differences between message bits extracted by the correct and incorrect keys, a stego key recovery method based on parametric hypothesis test is proposed. And then, formulas are given for calculating the sample size and threshold in the proposed stego key recovery model, on the basis of type I error and type II error. Experimental results show that the proposed method can successfully recover the stego key of OutGuess 0.13b, OutGuess0.2, JPEG domain random LSB matching steganography and random F3 steganography, the performance is superior to that of existing methods.

Keywords

Steganalysis Extraction attacks JPEG domain LSB steganography Stego key recovery 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant no. U1636219, U1736214, 61572052, 61602508, and 61772549), the National Key R&D Program of China (Grant no. 2016YFB0801303 and 2016QY01W0105), Plan for Scientific Innovation Talent of Henan Province (no. 2018JR0018), and the Key Technologies R&D Program of Henan Province (Grant no.162102210032).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jiufen Liu
    • 1
    • 2
  • Junjun Gan
    • 1
    • 2
  • Junchao Wang
    • 1
    • 2
  • Che Xu
    • 1
    • 2
  • Xiangyang Luo
    • 1
    • 2
    Email author
  1. 1.Zhengzhou Science and Technology InstituteZhengzhouChina
  2. 2.State Key Laboratory of Mathematical Engineering and Advanced ComputingZhengzhouChina

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