Bibliographic record
Abstract
Secret image sharing technique has been widely researched in the past decade. This technique allows us to create the share images from a secret image in such a way that an individual share does not reveal any information about the secret image, however when a specified number of shares are brought together, they can be used to reconstruct the secret image. In this paper, we first point out the weaknesses of the existing secret image sharing methods proposed by Thien and Lin [8] and Alharthi and Atrey [1], and then propose a new method that overcomes these weaknesses. Thien and Lin [8] use a permutation step which leads to disclosure of the secret image if the permutation key is revealed. Alharthi and Atrey [1] suggested an improvement over Thien and Lin's method by removing the permutation step. However, their method has a limitation that the first few shares are not usable because of its similarity with the secret image. We propose further improvement over these two methods by repeatedly changing the value of share number using a modulo prime function. To show the superiority of our method over others, we present the security analysis and experimental results.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".