RST invariant video watermarking based on log-polar mapping and phase-only filtering
Bibliographic record
Abstract
In this paper, we present a video watermarking algorithm based on the log-polar mapping and phase-only filtering method. The log-polar mapping (LPM) domain is obtained first from the magnitude of the Fourier spectrum of the frame. Then, the watermark pattern is embedded in the LPM domain based on the feature that rotation and scaling transformations in the spatial domain result in cyclically translational shifts in the logpolar mapping domain. A matching template is cut from the LPM domain after watermark embedding and is used for correlation matching to find the RST parameters for the watermarked video undergone geometric attacks. Our new phase-only filtering method is used and it is the only filter that can provide an acceptable discrimination while rotation or scaling or both applied to the watermarked video. A square portion from I-frame of each Group of Picture (GOP) is used for watermark embedding based on our analysis that a square image has better tolerance to rotation attack than a rectangular image. The experimental results demonstrate that this algorithm is robust against rotation, scaling, translation transform, noise addition, filtering, MPEG-2 compression, etc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".