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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".