On Joint Compression and Information Embedding When Watermarks and Covertexts Are Correlated
Bibliographic record
Abstract
A joint compression and public digital watermarking system with correlated watermark source and covertext source (M, S) is investigated in this paper. More specifically, for a given distortion level D between S and a watermarked signal X and a given compression rate Rcfor X, the watermark source M can be fully recovered with high probability at the end of a public watermark decoder after the watermarked signal is disturbed by a fixed memoryless attack channel p(y\x) if and only if H(M) lesmaxp(x,u\m,s)min{Rc- I(M, S; U, X) + I(M; U, Y), I(U; Y) - I(U; M, S) + I(M; U, Y)}, where the maximization is taken over all auxiliary random variables U (with finite alphabet U) and X jointly distributed with (M, S, X, Y) according to p(m, s, u, x, y) = p(m, s)p(u, x\m, s)p(y\x) such that Ed(S, X) les D
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".