RST Invariant Image Watermarking Algorithm With Mathematical Modeling and Analysis of the Watermarking Processes
Bibliographic record
Abstract
In this paper, a new rotation and scaling invariant image watermarking scheme is proposed based on rotation invariant feature and image normalization. A mathematical model is established to approximate the image based on the mixture generalized Gaussian distribution, which can facilitate the analysis of the watermarking processes. Using maximum a posteriori probability based image segmentation, the cover image is segmented into several homogeneous areas. Each region can be represented by a generalized Gaussian distribution, which is critical for the analysis of the watermarking processes mathematically. The rotation invariant features are extracted from the segmented areas and are selected as reference points. Sub-regions centered at the feature points are used for watermark embedding and extraction. Image normalization is applied to the sub-regions to achieve scaling invariance. Meanwhile, the watermark embedding and extraction schemes are analyzed mathematically based on the established mathematical model. The watermark embedding strength is adjusted adaptively using the noise visibility function and the probability of error is analyzed mathematically. The mathematical relationship between fidelity and robustness is established. The experimental results show the effectiveness and accuracy of the proposed scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".