Analysis and design of authentication watermarking
Bibliographic record
Abstract
This paper focuses on the use of nested lattice codes for effective analysis and design of semi-fragile watermarking schemes for content authentication applications. We provide a design framework for digital watermarking which is semi-fragile to any form of acceptable distortions, random or deterministic, such that both objectives of robustness and fragility can be effectively controlled and achieved. Robustness and fragility are characterized as two types of authentication errors. The encoder and decoder structures of semi-fragile schemes are derived and implemented using nested lattice codes to minimize these two types of errors. We then extend the framework to allow the legitimate and illegitimate distortions to be modelled as random noise. In addition, we investigate semi-fragile signature generation methods such that the signature is invariant to watermark embedding and legitimate distortion. A new approach, called MSB signature generation, is proposed which is shown to be more secure than the traditional dual subspace approach. Simulations of semi-fragile systems on real images are provided to demonstrate the effectiveness of nested lattice codes in achieving design objectives.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".