Adaptive‐capacity and robust natural language watermarking for agglutinative languages
Bibliographic record
Abstract
ABSTRACT We present a robust and adaptive‐capacity watermarking algorithm for agglutinative languages. All processes, including the selection of sentences to be watermarked, watermark embedding, and watermark extraction, are based on syntactic dependency trees. We show that it is more robust to use syntactic dependency trees than the surface forms of sentences in text watermarking. For the agglutinative languages, we embed watermark using the two main characteristics of the languages. First, because a word consists of several morphemes, we can watermark sentences using morphological division/combination without deep linguistic analysis. Second, they permit relatively free word order, so we can move a syntactic constituent within its clause. Finally, to increase the information‐hiding capacity, we adaptively compute the number of watermark bits to be embedded for each sentence. We perform three kinds of evaluation: perceptibility, robustness, and capacity of our method. High capacity is achieved by dynamically determining possibly embedded watermark bits for each sentence. The secret rank based on a syntactic dependency tree strengthens robustness of our method. Finally, we show that the displacement of syntactic constituents and morphological division/combination does not affect the style and naturalness of the text. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".