Watermark survival chance (WSC) concept for improving watermark robustness against JPEG compression
Bibliographic record
Abstract
This paper presents the new concept of watermark survival chance (WSC) for improving watermark robustness. WSC provides a robustness measure for an image feature (e.g. a discrete wavelet transform (DWT) coefficient) when used for watermark embedding, and thus can provide the watermark designer with prior knowledge on robust image features. As an illustrative example, we study additive spread spectrum watermarking in the DWT domain and consider JPEG compression as the attack. WSC is obtained for each DWT coefficient/subband for different compression ratios. Based on the WSC table for JPEG compression distortion, we suggest that: Wavelet coefficients can be divided into two main categories: block boundary coefficients and block non-boundary coefficients; block boundary coefficients generally are more robust for watermark embedding than block non-boundary coefficients; larger scale wavelet coefficients are generally more robust than smaller scales; a vertical subband is slightly preferred at small and large scales, while a horizontal subband is preferred at a medium scale.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".