An Improved Medical Image Watermarking Technique Based on Weber’s Law Descriptors
Bibliographic record
Abstract
In distributed m-health communication, it is a major challenge to develop an efficient blind watermarking method to protect the confidential medical data of patients. This paper proposes an efficient blind watermarking for medical images, which boasts a very high embedding capacity, a good robustness, and a strong imperceptibility. Three techniques, namely, discrete cosine transform (DCT), Weber’s descriptors (WDs), and Arnold chaotic map, were integrated to our method. Specifically, the Arnold chaotic map was used to scramble the watermark image. Then, the medical image was partitioned into non-over lapping blocks, and each block was subjected to DCT. After that, the scrambled watermark image data were embedded in the middle-band DCT coefficients of each block, such that two bits were embedded in each block. Simulation results show that the proposed watermarking method provides better imperceptibility, robustness, and computational complexity results with higher embedding capacity than the contrastive method.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".