A Robust Database Watermarking Scheme That Preserves Statistical Characteristics
Bibliographic record
Abstract
Database watermarking can be used for copyright verification and leakage traceability, effectively protecting the security of the database. However, the existing watermarking schemes commonly embed watermarks by modifying the original data, which changes the statistical characteristics and affects the statistical analysis of the database. Therefore, this paper proposes SCPW, aStatisticalCharacteristicsPreserving robust databaseWatermarking framework. First, we perform a theoretical analysis and propose a data modification scheme maintaining the statistical characteristics unchanged. Then, we establish the correspondence between the data and the watermarks that need to be embedded in it by grouping. Finally, the watermark message is embedded into the database through data verification and modification. Specifically, for data that needs to be watermarked, we first verify whether the potential watermark bits extracted from the data are the same as bits that need to be embedded. If they are the same, we regard this original data, usually a floating point number, as a “good number” and do not modify it. Otherwise, we modify the data until it becomes a “good number” using a data modification scheme that preserves the statistical characteristics proposed by the theoretical analysis. In addition, we also use the genetic algorithm to optimize the grouping results and increase the proportion of “good number”, thereby reducing the proportion of data that needs to be modified and further reducing distortion. To our best knowledge, SCPW is the first watermarking scheme that ensures the preservation of statistical characteristics, and the experimental results also prove its excellent ability to preserve statistical characteristics compared to existing schemes. Moreover, experiments also illustrate that our method is robust against a wide range of attacks. When under deletion attack (deletion rate = 90%), the bit error rate of watermark extraction is only 0.8%, which is more than 12% lower than the current best method.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| 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".