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Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction

A new technique for progressive visual secret sharing (PVSS) with adaptive priority weight is proposed in this paper. This approach employs the bitwise and eXclusive-OR (XOR) based approaches for generating a set of shared images from a single secret image. It effectively overcomes the former scheme...

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Autores principales: Prasetyo, Heri, Hsia, Chih-Hsien, Wicaksono Hari Prayuda, Alim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321329/
https://www.ncbi.nlm.nih.gov/pubmed/34460520
http://dx.doi.org/10.3390/jimaging7040070
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author Prasetyo, Heri
Hsia, Chih-Hsien
Wicaksono Hari Prayuda, Alim
author_facet Prasetyo, Heri
Hsia, Chih-Hsien
Wicaksono Hari Prayuda, Alim
author_sort Prasetyo, Heri
collection PubMed
description A new technique for progressive visual secret sharing (PVSS) with adaptive priority weight is proposed in this paper. This approach employs the bitwise and eXclusive-OR (XOR) based approaches for generating a set of shared images from a single secret image. It effectively overcomes the former scheme limitation on dealing with an odd number of stacked or collected shared images in the recovery process. The presented technique works well when the number of stacked shared images is odd or even. As documented in experimental results, the proposed method offers good results over binary, grayscale, and color images with a perfectly reconstructed secret image. In addition, the performance of the proposed method is also supported with theoretical analysis showing its lossless ability to recover the secret image. However, it can be considered as a strong substitutive candidate for implementing a PVSS system.
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spelling pubmed-83213292021-08-26 Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction Prasetyo, Heri Hsia, Chih-Hsien Wicaksono Hari Prayuda, Alim J Imaging Article A new technique for progressive visual secret sharing (PVSS) with adaptive priority weight is proposed in this paper. This approach employs the bitwise and eXclusive-OR (XOR) based approaches for generating a set of shared images from a single secret image. It effectively overcomes the former scheme limitation on dealing with an odd number of stacked or collected shared images in the recovery process. The presented technique works well when the number of stacked shared images is odd or even. As documented in experimental results, the proposed method offers good results over binary, grayscale, and color images with a perfectly reconstructed secret image. In addition, the performance of the proposed method is also supported with theoretical analysis showing its lossless ability to recover the secret image. However, it can be considered as a strong substitutive candidate for implementing a PVSS system. MDPI 2021-04-03 /pmc/articles/PMC8321329/ /pubmed/34460520 http://dx.doi.org/10.3390/jimaging7040070 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Prasetyo, Heri
Hsia, Chih-Hsien
Wicaksono Hari Prayuda, Alim
Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title_full Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title_fullStr Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title_full_unstemmed Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title_short Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
title_sort progressive secret sharing with adaptive priority and perfect reconstruction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321329/
https://www.ncbi.nlm.nih.gov/pubmed/34460520
http://dx.doi.org/10.3390/jimaging7040070
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