Meaningful Secret Image Sharing with Uniform Image Quality

Jingwen Cheng (), Lintao Liu, Feng Chen and Yue Jiang
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Jingwen Cheng: College of Electronic Engineering, National Universityof Defense Technology, Hefei 230037, China
Lintao Liu: College of Electronic Engineering, National Universityof Defense Technology, 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

Mathematics, 2022, vol. 10, issue 18, 1-14

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.

Keywords: secret image sharing; polynomial-based SIS; meaningful shadows; saliency detection; uniform image quality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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