Adaptive contourlet-based image watermarking robust to geometric transformations and image compression
Bibliographic record
Abstract
This paper proposes a new image watermarking scheme robust to geometric transformations and image compression. The approach presented here is based on contourlet transform (CT) and principal component analysis (PCA). In this scheme, the largest contourlet coefficients of the last directional subband of the cover image are selected as the embedding location. The principal components of contourlet coefficients are used to embed the watermark. Meanwhile, with the noise visibility function (NVF), the watermark embedding strength is adjusted adaptively to preserve the perceptual quality of the image. The watermark can be detected with high accuracy after various possible distortions. Moreover, the scheme can be extended to be blind for different applications. The experimental results demonstrate that the scheme has good performance in terms of both fidelity and robustness against a variety of image-processing attacks, such as rotation, scaling, translation (RST) and image compression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".