Bibliographic record
Abstract
This paper presents a new scheme for data hiding with robustness to rescale and rotation distortions. The most common geometrical distortion rescale factor and rotation factor during image manipulation can be easily estimated by comparing feature points location of original and distorted images. However, the decoder still has to have this prior information regarding the feature points of original image which is not practical. In this paper, two color spaces of RGB images can be considered as two independent channels. One is synchronization channel (SC) which transmit the prior information of original image, and another is communication channel (CC) carrying the hiding data. Contend-based image watermarking method is adopted in SC channel. The feature point extractor plays key role in our scheme. Image units are represented by Delaunay tessellations constructed from extracted feature points. The scale factor and rotation angle estimated from feature points are parsed into binary data as synchronization information and embedded to each image unit. The synchronization information can be extracted successfully if at least two image units are robustness against the distortion. Consequently, the rescaling factor and the rotation angle can be estimated and corrected. Several key problems such as the feature point detector improvement, image unit design and SC synchronization information hiding procedure are discussed in details. The experiment results and discussions are given as well
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".