A secret image sharing scheme with improved embedding capacity and a deception recognition mechanism
Bibliographic record
Abstract
Image hiding is a technique for transmitting secret information under the cover of a digital image.It usually conceals sensitive information into images for the purpose of encryption.Currently, high embedding capacity and information security remain important research aspects of the image hiding.In this study, a secret image sharing scheme based on a reference matrix is proposed to enhance embedding capacity and verify data integrity.In the proposed scheme, a hill matrix is designed as a reference matrix and a location table is generated.Moreover, a location pair table is generated to ensure the uniqueness of data hiding locations.Then, leveraging the processing of the location pair table, as well as the mapping of the reference matrix and the location table, each pixel pair is exploited to conceal eight secret bits.Furthermore, based on the special construction of the hill matrix, a deception recognition mechanism is designed.This mechanism can detect deceptive behavior and identify tampered images by means of data hiding locations.The experimental results indicate that the proposed scheme achieves a higher embedding capacity and better deception recognition performance than that of most of existing schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".