A wavelet transform based digital image watermarking scheme
Bibliographic record
Abstract
Digital image watermarking techniques have been proposed to prevent unauthorized distribution of multimedia data. A digital watermark encodes the owner's license information and embeds it into the image. Several discrete wavelet transform (DWT) based techniques are used for watermarking. In this paper, a watermarking scheme is proposed in which the image is decomposed into wavelet coefficients and a visual recognizable logo is embedded in the wavelet coefficients. Wavelet coefficients corresponding to the points located in a neighborhood that have maximum entropy are proposed for embedding the watermark. This method embeds the maximum amount of watermark while the watermark is imperceptible. Watermarking techniques must be robust to some attacks such as smoothing, sharpening and compression. These maximum entropy areas can survive a variety of attacks and can be used as reference points for watermark embedding. The experimental results confirmed that the technique is robust to a variety of attacks.
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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 | 0.000 | 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 | 0.002 |
| 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".