An Adaptive Compressed MPEG-2 Video Watermarking Scheme
Bibliographic record
Abstract
Digital watermarking is becoming more and more important for protecting the authenticity of multimedia objects as they become easier to copy, exchange, and modify. Several watermarking schemes have been proposed in recent years, but most of them deal with still images, only some being extended over to the temporal domain for video watermarking. But again most of those approaches are applied to uncompressed video processing domain. In the subject paper, a new compressed video watermarking procedure is proposed. The developed method embeds several binary images, decomposed from a single watermark image, into different scenes of a video sequence. The spatial spread spectrum watermark is embedded directly into the compressed bit streams by modifying discrete cosine transform (DCT) coefficients. In order to embed the watermark with minimum loss in image fidelity, a visual mask based on local image characteristics is incorporated. Extensive experimental simulations demonstrate that the proposed watermarking scheme is substantially more effective and robust against spatial attacks such as scaling, rotation, frame averaging, and filtering, besides temporal attacks like frame dropping and temporal shifting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".