Bibliographic record
Abstract
Based on log-polar mapping (LPM), this paper presents a novel digital image watermarking scheme that is invariant to rotation, scaling, and translation (RST). We embed watermark in the log-polar mapping (LPM) of the Fourier magnitude spectrum of original image to simplify the effects of RST transformations into simple shifts. For watermark embedding, the approximate ILPM is employed to replace ILPM, in order to eliminate the imprecision caused by ILPM. Therefore actually watermarks are embedded in the Fourier magnitude spectrum of the original image, to achieve the effect of being embedded in LPM domain. Watermark extraction does not need original image, we use exhaustive search in the embedding area to handle the shift of watermark positions in LPM domain caused by rotation and scaling in spatial domain. The scheme avoids computing inverse log-polar mapping (ILPM) to preserve the image quality. Evaluations demonstrate that the scheme is invariant to rotation and translation, sufficiently invariant to scaling transformation, and very robust to JPEG compression and other attacks.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".