A Secret Key Based Multiscale Fragile Watermark in the Wavelet Domain
Bibliographic record
Abstract
The distribution of the wavelet coefficients in 2-D discrete wavelet transform (DWT) subspaces can be well described by a Gaussian mixture statistical model. In this paper, a secret key based fragile watermarking scheme is presented based on this statistical model. The Gaussian statistical model parameters are obtained by an expectation maximization (EM) algorithm and modified in a way to form special relationships for image authentication. The secret key is designed to securely embed a message bit stream, such as personal signatures or copyright logos, into a host image. Because of the secret embedding key, the new method is robust to most image tampering, even when the attackers are fully aware of the watermark embedding algorithms. Besides, the secret embedding key can be encrypted and embedded as a robust watermark into the same host image of the fragile watermarks for the benefit that the decoding of fragile watermarks only requires a single encryption key other than the image itself. The new method also has the advantage of changing only a few image data for watermark embedding and being able to distinguish some normal image operations such as compression from malicious to achieve a semi-fragile application
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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 | 0.000 |
| 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".