A hidden Markov model-based blind detector for multiplicative watermarking
Bibliographic record
Abstract
Nowadays, transmission of data via Internet has made illegal data distribution a major problem in digital world. Watermarking is known as a possible solution to protect digital data. In this work, we propose a blind detector for multiplicative watermarking of images in the wavelet domain. To this end, the vector-based hidden Markov model (HMM) is employed as a prior model for the wavelet coefficients of the host image. This model is known to provide an accurate fit to the distribution of the wavelet coefficients by capturing both their heavy-tailed marginal statistics and their inter-subbands and cross-orientations dependencies. Analytical expressions for the proposed watermark detector such as the mean and variance of the log-likelihood ratio test are derived and used to evaluate its performance. The performance of the proposed detector is shown to outperform that of the other detectors by providing higher detection rate and better imperceptibility of the embedded watermark. It is also shown that the proposed vector-based HMM detector under various attacks such as compression, rotation, filtering and noise, is more robust than other existing detectors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".