Fragile watermark based on the Gaussian mixture model in the wavelet domain for image authentication
Bibliographic record
Abstract
In this paper, a new fragile watermarking method based on statistical analysis in the wavelet domain is developed for image authentication. A two component Gaussian mixture model is developed to describe the statistical characteristics of images in the wavelet domain. Each wavelet subspace of the original image is divided into a watermarking block and a reference block. A Gaussian mixture model is then applied to both blocks to obtain their respective model parameters by an EM (expectation-maximization) algorithm. By slightly changing the wavelet coefficients (adding watermark) in the watermark block, we can adjust its model parameter to the same value as that of the reference block for authentication purposes, which constitutes the fragile watermark. Any change in the fragile-watermarked image will break the relationship of the statistical models between the watermarking block and reference block. The authentication procedure needs only a simple comparison between the model parameters of the two blocks in the watermarked image and no information about the original image. The preliminary experimental results indicate that the new watermarking scheme conforms with human perception characteristics and provides a perceptually invisible fragile watermark with fewer image data modified, compared with some other conventional fragile watermarking methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".