Robust Content-Based Digital Image Watermarking Scheme in Steerable Pyramid Domain
Bibliographic record
Abstract
In the digital world in which we are living, the intellectual property protection becomes a concern especially with the proliferation of files transfer over networks. The ability to access data such as text, images, video, and audio has become quicker and easier for people with little to no knowledge of technology. In this paper, a robust watermarking scheme based on the original image content is proposed and simulated. Steerable pyramid transform is used as an embedding domain to its good spatial-frequency characteristics, and its wide applications in the image/video coding standards. The embedding process aims to insert some information in a digital document to identify its owner later. This process requires the original image to be protected and the watermark image related to the image’s owner. It needs also a threshold value used by Sobel-Feldman operator to extract the original image features. The embedding of the watermark image is performed in high frequency components of the original image. Experimental evaluation demonstrates that the proposed watermarking scheme is able to withstand a variety of attacks and at the same time provide good visual quality of the watermarked image.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".