A multiscale fragile watermark based on the Gaussian mixture model in the wavelet domain
Bibliographic record
Abstract
The wavelet coefficients in 2D discrete wavelet transform (DWT) subspaces have a peaky, heavy-tailed marginal distribution that can be well described by a Gaussian mixture statistical model. In this paper, a multiscale implementation of fragile watermarks based on the Gaussian mixture model is presented. The presented new method can embed a message bit stream, such as personal signatures or copyright logos, into a host image. With the embedded message bits spreading over the whole image area, the new method can detect and localize any image tampering since it will inevitably destroy a certain message bits. Compared with some other fragile watermark techniques, the statistical model based method modifies only a very small amount of image data to embed watermarks and the modification is hardly perceived by human vision because it occurs at texture edges. Besides, the multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as compression from malicious attacks, which is meaningful in terms of semi-fragile watermarking applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".