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Meaningful Secret Image Sharing with Uniform Image Quality

Author

Listed:
  • Jingwen Cheng

    (College of Electronic Engineering, National Universityof Defense Technology, Hefei 230037, China
    Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China)

  • Lintao Liu

    (College of Electronic Engineering, National Universityof Defense Technology, Hefei 230037, China
    Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China)

  • Feng Chen

    (College of Air Defense and Anti-Missile, Air Force Engineering University, Xi’an 710051, China)

  • Yue Jiang

    (College of Electronic Engineering, National Universityof Defense Technology, Hefei 230037, China
    Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China)

Abstract

In meaningful secret image sharing (MSIS), a secret image is divided into n shadows. Each shadow is meaningful and similar to the corresponding cover image. Meaningful shadows can reduce the suspicion of attackers in transmission and facilitate shadow management. Previous MSIS schemes always include pixel expansion, and cross-interference from different shadows may exist when cover images are extremely unnatural images with large black and white blocks. In this article, we propose an MSIS with uniform image quality. A threshold t is set to determine the absolute salient regions. More identical bits are allocated according to saliency values in the absolute saliency region, which can improve image quality. In addition, the new identical bits allocation strategy also adjusts the randomness of the shadow images, generating shadows with uniform image quality and avoiding the cross-interference between different shadows. Experimental results show the effectiveness of our proposed scheme.

Suggested Citation

  • Jingwen Cheng & Lintao Liu & Feng Chen & Yue Jiang, 2022. "Meaningful Secret Image Sharing with Uniform Image Quality," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3241-:d:908268
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    Cited by:

    1. Tao Zou & Guozhang Li & Ge Ma & Zhijia Zhao & Zhifu Li, 2022. "A Derivative Fidelity-Based Total Generalized Variation Method for Image Restoration," Mathematics, MDPI, vol. 10(21), pages 1-12, October.

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