WH-SVD-Cb: A Robust Blind Watermarking Scheme Using Wavelet Transform and Hessenberg SVD with Arnold Chaotic Map in the Cb Channel
Bibliographic record
Abstract
With the significant growth of malicious attacks, safeguarding personal data has become a critical and pressing concern.Images transmitted over unsecured networks are particularly vulnerable to tampering and unauthorized distribution.To address these challenges, networks must be fortified with robust strategies capable of preventing new attacks.These strategies should prioritize enhanced performance while maintaining content fidelity.The article presents a blind approach for color image watermarking that leverages the YCbCr color space's key properties.Both the image and the watermark were converted from RGB to YCbCr.Afterward, the watermark is encrypted using the Arnold Chaotic Map (ACM) to strengthen privacy and embedded into the Cb component using the WH-SVD-Cb watermarking schema.Image authenticity is validated through blind watermark extraction.The method's robustness and imperceptibility are analyzed through empirical evaluations.The findings indicate that embedding of watermark in the Cb component achieves notable robustness with NC values closing 1 and imperceptibility exceeding 52 dB, all within a processing time of 0.224 seconds.A demonstration code for the proposed watermarking scheme is available: https://www.mathworks.com/matlabcentral/fileexchange/177859-
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".