Application of Empirical Wavelet Transform in Digital Image Watermarking
Bibliographic record
Abstract
Nowadays, rapid growth of the Internet and digital multimedia technologies make it possible to duplicate data without any loss of quality and at a very low cost. In this regard, manipulation of documents will easily be accomplished by applying digital art and without the copyright owner's permission. To deal with situation, more diverse security requirements are introduced every day. Watermarking is considered as one of the methods used for achieving this purpose. Watermarking is focused on inserting a subtle signal between the host media data somehow it does not change the original data, but they can be extracted if necessary. They are also used as a claim for ownership of the digital effect. Various methods have been presented for watermarking so far. These methods have been developed to overcome the weaknesses of previous methods. Empirical wavelet transform is taken into account as a new method for hiding and extracting military digital images with respect to alpha composition. As such, it is resistant to noise, low pass filter and compression. Analytical studies showed that this method is more efficient than other methods from quantitative and qualitative point of view.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".