Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7677–7698 | Cite as

A secure image sharing scheme with high quality stego-images based on steganography

  • Junhui HeEmail author
  • Weiqiang Lan
  • Shaohua Tang


Image sharing can be utilized to protect important commercial, military or private images against a single point of failure. Many existing image sharing schemes may have one or more of the security weaknesses as follows: First, noise-like image shares may easily arouse the attackers’ attention; Second, cheating in the recovery of the secret image cannot be prevented effectively; Third, the requisite size of cover images may be very large; Finally, poor quality of the stego-images may lessen camouflage effects. In this paper, a novel secure image sharing scheme with high quality stego-images is proposed. With the use of LOCO-I compression as a preprocessing approach, the statistical correlations between neighboring pixels of a secret image drop significantly, which may greatly enhance the visual security of the proposed scheme. And the necessary size of cover images is reduced. Moreover, the PSNR values of stego-images are much higher than the related works. In order to detect three kinds of deception during secret image reconstruction, the hash-based message authentication codes of an image share, the value of argument x and the identity ID of a participant are embedded into a cover image together with the image share. In addition, the application of dynamic embedding with a random strategy further enhance the security of our scheme.


Image sharing Steganography LOCO-I compression Message authentication code Dynamic embedding 



The authors thank the anonymous reviewers for their insightful comments and suggestions. This work was partially supported by the 973 Program (No. 2014CB360501), the National Natural Science Foundation of China (No. U1135004 and No. 61170080), Guangdong Provincial Natural Science Foundation (No. 2014A030308006), and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2011).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of Technology, Guangzhou Higher Education Mega CentreGuangzhouPeople’s Republic of China

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