Robust Blind Watermarking Method for High Capacity RGB Image in Wavelet Domain
Bibliographic record
Abstract
Image watermarking is a tool to maintain the authentication and copyright protection of digital documents.RGB image is one of the media that is extensively distributed and transferred by the cloud.This type of digital data can be protected by hiding watermark protection logos.Furthermore, this type of images presents a media that can be used to hide more than one watermark.In this paper, a novel RGB image watermarking method based on DWT is used to embed three watermark logos, instead of one watermark logo.In each color channel, a watermark logo is embedded to increase the authentication criteria and capacity payload.To improve the robustness and invisibility, the wavelet transform is exploited to hide the watermark data in low-frequency bands rather than the pixel values.The proposed method is evaluated using three metrics, PSNR, NCC, and HD.The robustness of the proposed method is tested under various attack types, such as the noise, filter, and sharpening attacks.The experimental results show that the proposed RGB watermarking method has a good trade-off between robustness and invisibility and resistance to several attacks in the watermarked image.The method has been evaluated against various attacks (e.g., noise, filtering, compression, sharpening), demonstrating strong robustness while maintaining high image quality and achieving PSNR values between 37.8 and 51.2 dB.The proposed scheme outperforms several existing DWT-based RGB watermarking methods, showing a better trade-off between robustness, imperceptibility, and watermark capacity.
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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) | 0.001 | 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 | 0.001 | 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".