A Novel Look-Up Table Design Method for Data Hiding With Reduced Distortion
Bibliographic record
Abstract
Look-up table (LUT)-based data hiding is a simple and efficient technique to hide secondary information (watermark) into multimedia work for various applications such as copyright protection, transaction tracking or content annotation. This paper studies the distortion introduced by a general LUT-based data hiding. We find that designing LUT according to the distribution of host data and watermark data can greatly reduce the distortion of LUT embedding. A new practical reduced-distortion LUT design method is developed for robust data hiding. The new method is applied in a wavelet domain image data hiding system and only significant wavelet coefficients are used to embed the watermark. A Gaussian mixture model and a related expectation-maximization algorithm-based method are employed to model the statistical distribution of the host image. The statistical model is used to select significant coefficients of the host image for data hiding. The experimental results show that compared to the conventional odd-even LUT embedding method, the presented new LUT data hiding algorithm provides average 1. 5-2. 5 dB PSNR improvement and better robustness for image watermarking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".