Loading [MathJax]/extensions/MathMenu.js
SE-SIS: Shadow-Embeddable Lossless Secret Image Sharing for Greyscale Images | IEEE Conference Publication | IEEE Xplore

SE-SIS: Shadow-Embeddable Lossless Secret Image Sharing for Greyscale Images


Abstract:

Secret image sharing (SIS) has made significant progress in research and has found wide applications. However, we note that shadows of traditional SIS contain a large amo...Show More

Abstract:

Secret image sharing (SIS) has made significant progress in research and has found wide applications. However, we note that shadows of traditional SIS contain a large amount of redundancy. A novel Shadow-Embeddable Secret Image Sharing scheme (SE-SIS) leveraging the redundancy in the shadows is proposed in this paper. SE-SIS utilizes the random values in Lagrange polynomials of traditional secret image sharing (SIS) scheme, and modifies a shadow to embed another secret image with a secret key. Then other shadows are modified simultaneously according to the properties of Lagrange polynomials to ensure the accurate recovery of the previously shared image. It is worth noting that embedding process does not impact the recovery of the shared image, and the embedded shadow is indistinguishable from the others. SE-SIS modifies noise-liked shadows into other noise-liked ones without affecting the recovery process, thereby achieving a high embedding rate. Meanwhile, SE-SIS realizes lossless recovery for both the shared image and the secret image. Experimental results indicate SE-SIS constructs randomized shadows and exhibits excellent performance in terms of Peak Signal to Noise Ratio (PSNR) and embedding rate.
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
ISBN Information:

ISSN Information:

Conference Location: Seoul, Korea, Republic of

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.