Towards Shift Tolerant Visual Secret Sharing Schemes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".