Bibliographic record
Abstract
M-Ary Phase Modulation for Digital Watermarking In spread spectrum based watermarking schemes, it is a challenging task to embed multiple bits of information into the host signal.M-ary modulation has been proposed as an effective approach to multibit watermarking. It has been proved that anM-ary modulation based watermarking system outperforms significantly a binary modulation based watermarking system. However, in the existingM-ary modulation based algorithms, the value ofMis restricted to be less than 256, because asMincreases, the computation workload for data extraction advances exponentially. In this paper, we propose an efficientM-ary modulation scheme, i.e.,M-ary phase modulation, which reduces the computation in data extraction to a very low level. With this scheme, it is practical to implement anM-ary modulation based algorithm with a high value ofM, e.g.,M= 220. This is significant for a watermarking system, because it can either greatly increase the data capacity of a watermark given the necessary watermark robustness, or considerably improve the watermark robustness given the amount of information of the watermark. The superiority of the proposed scheme is verified by simulation 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".