Motion-Frames Based Video Watermarking Scheme for Copyright Protection Using Guided Filtering in Wavelet Domain
Bibliographic record
Abstract
Due to the dawn of elevated bandwidth of the internet, there exists an explosion of multimedia data exchange that makes our everyday life extremely easier.With this ease, several security issues also arise named as ownership identification, copyright protection, and illegal access to digital content.This research article proposes a prevailing technique under the scope of video watermarking based on the wavelet transform domain method with the inclusion of an explicit frame filter known as a guided filter for protecting the copyrights.The guided filter is the product of a linear model and considers the resultant image to generate the ultimate filtered output.This filter is significantly accepted for its edgepreservation and detail enhancement characteristics.Thus, the inclusion of a guided filter with the watermarking technique can efficiently confiscate the video frame noise and expresses the descriptive facets of the video object resulting in an enhanced version of the watermarked video frame.Furthermore, the said filter is considered the fastest edgepreserving filter as it exhibits a fast and fairly accurate time algorithm, in spite of the other supportive attributes like the intensity and size of the kernels.Therefore, the outcome of the investigation shows that the video watermarking approach involving wavelet transform embedded with guided filter on motion-frames of the video object is proficient and effective in a range of video watermarking application areas, including copyright protection, ownership identification, video authentication, etc.
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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 | 0.001 | 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".