On Information Embedding When Watermarks and Covertexts Are Correlated
Bibliographic record
Abstract
A new digital watermarking scenario is studied, where a watermark M correlated with a covertext S is to be transmitted by embedding M into S. The configuration of this scenario is different from that treated in existing digital watermarking works, where watermarks are assumed independent of covertexts. Assume that the pair (M, S) is drawn from an independently and identically distributed sequence. A necessary and sufficient condition is derived under which the watermark M can be recovered with high probability at the end of a watermark decoder after the watermarked signal is disturbed by a fixed memoryless attack channel p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y|X</sub> (y|x). Specifically, it is shown that in the case of public watermarking where the covertext S is not accessible to the watermark decoder, M can be recovered with high probability if and only if H(M) les max <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p(x,u|m,s):Ed(S,X)lesD</sub> [I(U);M,S) - I(U; M, S) + I(M;U,Y)], where the maximum is taken over all auxiliary random variables U and X jointly distributed with M and S and satisfying Ed(S, X) les D. In particular, the result implies that the Shannon separation theorem can not be extended to this scenario, that is, it is still possible to transmit M reliably even when H(M) is strictly greater than the watermarking capacity. A similar result is also established for combined source coding and Gel'fand Pinsker channel coding
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| 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".