Joint robust watermarking and compression using variable-rate scalar quantization
Bibliographic record
Abstract
In this paper we are interested in the design of joint watermarking and compression (JWC) systems to obtain an efficient tradeoff among the embedding rate, composite rate, encoding distortion, and robustness. Using variable-rate scalar quantization (VRSQ) for watermarking and compression, we first present an optimum binary JWC encoding algorithm to maximize the robustness of the systems in the presence of additive Gaussian attacks under constraints on both of the composite rate and encoding distortion, and then show the convergence of the algorithm. It is further demonstrated by simulation that in the distortion-to-noise ratio (DNR) region of practical interest, optimal JWC systems designed using VRSQ achieve about 0:3-dB DNR gain over optimal JWC systems using fixed-rate scalar quantization (FRSQ) for an independent and identically distributed (i.i.d.) Gaussian source. As an application of JWC design using VRSQ to image watermarking and compression, a joint odd-even watermarking (OEW) and JPEG compression scheme is developed and demonstrated to achieve better embedding performance than the JPEG-compatible differential energy watermarking (DEW) scheme and the differential quantization watermarking (DQW) scheme developed in the recent literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".