A Tabu Search Algorithm for General Threshold Visual Cryptography Schemes
Bibliographic record
Abstract
In Visual Cryptography Schemes (VCSs), for message n transparencies are generated, such that the original message is visible if any k of them are stacked. VCS especially for large values of k and n, the pixel expansion’s reduction and enhancement of the recovered images’ display quality continue to be critical issues. In addition to this, it is challenging to develop a practical and systematic approach to threshold VCSs. An optimization-based pixel-expansion-free threshold VCSs approach has been proposed for binary secret images’ encryption. Along with contrast, blackness is also treated as a performance metric for assessing the recovered images’ display quality. An ideally secure technique for a secret image’s protection through its partition into shadow images (known as shadows) is the Visual Secret Sharing (VSS) scheme. Acquirement of a smaller shadow size or a higher contrast is the VSS schemes’ latest focus. The white pixels’ frequency has been utilized to demonstrate the recovered image’s contrast in this work. While the Probabilistic VSS (ProbVSS) scheme is non-expansible, it can also be readily deployed depending upon the traditional VSS scheme. Initially, this work has defined the problem as a mathematical optimization model such that, while contingent on blackness and density-balance constraints, there is the maximization of the recovered images’ contrast. Afterward, an algorithm dependent on the Tabu Search (TS) is devised in this work for this problem’s resolution. Multiple complicated combinatorial problems have been successfully resolved with the powerful TS algorithm. Moreover, this work has attempted to bolster the contrast through the density-balance constraint’s slight relaxation. Compared to the older techniques, the proposed optimization-based approach is superior regarding the recovered images’ display quality and the pixel expansion factor from the experimental outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".