Real-time video watermarking on programmable graphics hardware
Bibliographic record
Abstract
In this paper, we propose a real-time video watermarking system on programmable graphics hardware. Real-time video watermarking is important to the use of digital video in legal proceedings, security surveillance, new reportage and commercial video transactions. The watermarking scheme implemented here is based on Wong's scheme for image watermarking, and is designed to detect and localize any change in the pixels of any frame of the incoming video stream. We implement this scheme for real-time operation on programmable graphics hardware. The graphics processing units (GPUs) found on many modern commodity-level graphics cards have the ability to execute application-defined sequences of instructions on not only geometric primitives, defined by vertices, but also on image or texture fragments mapped to rasterized geometric primitives. These fragment programs, also known as fragment or pixel shaders, execute in hardware and in parallel on the GPU for each fragment, or pixel, that is rendered, making the GPU well suited for image and video processing. We illustrate real-time performance, low perceptibility, and good bit-error rates and localization by way of a general testing framework that allows straightforward testing of any video watermarking system implemented on programmable graphics hardware.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".