Partial Secret Image Sharing for (n, n) Threshold Based on Image Inpainting View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-12-30

AUTHORS

Xuehu Yan , Yuliang Lu , Lintao Liu , Shen Wang , Song Wan , Wanmeng Ding , Hanlin Liu

ABSTRACT

Shamir’s polynomial-based secret image sharing (SIS) scheme and visual secret sharing (VSS) also called visual cryptography scheme (VCS), are the primary branches in SIS. In traditional (k, n) threshold secret sharing, a secret image is fully (entirely) generated into n shadow images (shares) distributed to n associated participants. The secret image can be recovered by collecting any k or more shadow images. The previous SIS schemes dealt with the full secret image neglecting the possible situation that only part of the secret image needs protection. However, in some applications, only target part of the secret image may need to be protected while other parts may be not in a full image. In this paper, we consider the partial secret image sharing (PSIS) issue as well as propose a PSIS scheme for (n, n) threshold based on image inpainting and linear congruence (LC). First the target part is manually selected or marked in the color secret image. Second, the target part is automatically removed from the original secret image to obtain the same input cover images (unpainted shadow images). Third, the target secret part is generated into the pixels corresponding to shadow images by LC in the processing of shadow images texture synthesis (inpainting), so as to obtain the shadow images in a visually plausible way. As a result, the full secret image including the target secret part and other parts will be recovered losslessly by adding all the inpainted meaningful shadow images. Experiments are conducted to evaluate the efficiency of the proposed scheme. More... »

PAGES

527-538

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-71598-8_47

DOI

http://dx.doi.org/10.1007/978-3-319-71598-8_47

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100108192


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