New Informed Non-Blind Medical Image Watermarking Based on Local Binary Pattern
Bibliographic record
Abstract
Medical image watermarking represents a promising alternative tool regarding many security aspects such: digital rights, authenticity and integrity and content protection issues. Achieving a successful watermarking should be achieved by choosing the most significant and important patterns describing the image. This strategy should also ensure a tradeoff between the robustness of the watermark against attacks and the computational time both for watermark embedding and extracting processes. In this paper, an informed medical image watermarking scheme is proposed based on local binary pattern LBP. Local Binary Pattern (LBP) is an effective texture descriptor for images by providing the texture regions of interests concerned by watermarking. A watermark is built based on the significant information extracted from the host image by through the LBP descriptor. LBP image will be addressed to be embedded using a linear interpolation. Scenarios of geometric and non-geometric attacks have been realized on the watermarked images to evaluate the robustness of the embedded watermark in the extraction process. Furthermore, the obtained experiment results show the effectiveness of the proposed approach regarding the watermark imperceptibility and robustness.
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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 | 0.001 |
| Research integrity | 0.001 | 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".