Why this work is in the frame
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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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it