RST invariant video watermarking based on 1D DFT and Radon transform
Bibliographic record
Abstract
In this paper, we propose a new video watermarking algorithm robust against geometric attacks, such as, format aspect ratio change, rotation, cropping, and translation. The watermarking algorithm is also robust against other attacks, such as frame swapping, frame dropping, noise addition, filtering, and compression. This algorithm is scene based. The scene detection is applied to segment a video sequence into different scenes before watermark embedding. The histogram difference is used for scene change detection. 1D Discrete Fourier transform (DFT) within temporal domain is applied in a group of picture (GOP). In the temporal frequency domain, we select the high frequency frame as our watermark embedding frame. We embed a random generated watermark pattern into the ID projection of this frame. The blue channel is the only channel we consider for embedding based on the fact that human visual system is least sensitive to this component. The experimental results demonstrate the feasibility of the algorithm.
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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 | 0.001 |
| 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".