Computer Science – Cryptography and Security
Scientific paper
2010-04-14
Computer Science
Cryptography and Security
Scientific paper
In (k, n) visual secret sharing (VSS) scheme, secret image can be visually reconstructed when k or more participants printing theirs shares on transparencies and stack them together. No secret is revealed with fewer than k shares. The alignment of the transparencies is important to the visual quality of the reconstructed secret image. In VSS scheme, each pixel of the original secret image is expanded to m sub-pixels in a share image. If a share image is printed on paper with the same size as the original secret image, the alignment or the registration of the sub-pixels, which is only m times smaller than that in the original secret, could be troublesome. Liu et al. [4] has noticed this alignment problem and observed that some information of the secret image may still be revealed even when the shares are not precisely registered in the horizontal direction. Yang et al. [9] introduced a general approach to construct a misalignment tolerant (k, n)-VSS scheme using big and small blocks for the situation where the original secret image has a certain degree of redundancy in shape accuracy. In this paper, we propose a (2, n)-VSS scheme that allows a relative shift between the shares in the horizontal direction and vertical direction. When the shares are perfectly aligned, the contrast of the reconstructed image is equal to that of traditional VSS shceme. When there is a shift, average contrast in the reconstructed image is higher than that of the traditional VSS scheme, and the scheme can still work in the cases where very little shape redundancy presents in the image. The trade-off is that our method involves a larger pixel expansion. The basic building block of our scheme is duplication and concatenation of certain rows or columns of the basic matrices. This seemingly simple but very powerful construction principle can be easily used to create more general (k, n)-VSS schemes.
Dong Lin
Li Xiaobo
Wang Dao-Shun
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