HCSS-GB and IBESS: Secret Image Sharing Schemes with Enhanced Shadow Management and Visual-Gradient Access Control

HCSS-GB 和 IBESS:具有增强型阴影管理和视觉梯度访问控制的秘密图像共享方案

阅读:2

Abstract

Image protection in privacy-sensitive domains, such as healthcare and military, exposes critical limitations in existing secret image sharing (SIS) schemes, including cumbersome shadow management, coarse-grained access control, and an inefficient storage-speed trade-off, which limits SIS in practical scenarios. Thus, this paper proposes two SIS schemes to address the above issues: the hierarchical control sharing scheme with Gaussian blur (HCSS-GB) and the image bit expansion-based sharing scheme (IBESS). For scenarios with limited storage space, HCSS-GB employs Gaussian blur to generate gradient-blurred cover images and integrates a controllable sharing model to produce meaningful shadow images without pixel expansion based on Shamir's secret sharing. Furthermore, to accommodate real-time application scenarios, IBESS employs bit expansion to combine the high bits of generated shadow images with those of blurred carrier images, enhancing operational efficiency at the cost of increased storage overhead. Experimental results demonstrate that both schemes achieve lossless recovery (with PSNR of ∞, MSE of 0, and SSIM of 1), validating their reliability. Specifically, HCSS-GB maintains a 1:1 storage ratio with the original image, making it highly suitable for storage-constrained environments; IBESS exhibits exceptional efficiency, with sharing time as low as 2.1 s under the (7,8) threshold, ideal for real-time tasks. Comparative analyses further show that using carrier images with high standard deviation contrast (Cσ) and Laplacian-based sharpness (SL) significantly enhances shadow distinguishability, strengthening the effectiveness of hierarchical access control. Both schemes provide valuable solutions for secure image sharing and efficient shadow management, with their validity and practicality confirmed by experimental data.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。