A Wavelet-PCA-Based Fingerprinting Scheme for Peer-to-Peer Video File Sharing
Bibliographic record
Abstract
In order to utilize peer-to-peer (P2P) networks in legal content distribution to benefit the legal content providers, copyright protection needs to be enhanced. In this paper, a fingerprint generation and embedding method is proposed for complex P2P file sharing networks. In this method, wavelet and principal component analysis (PCA) techniques are used for fingerprint generation. First, the wavelet technique obtains a low-frequency representation of the test image (or source file, which is assumed to be one I frame of a video with a DVD quality) and PCA finds the features of the representation. Then, a set of fingerprint matrices can be created based on a proposed algorithm. Finally, each matrix combines with the low-frequency representative to become a unique fingerprinted matrix. The fingerprinted matrix is not only much smaller than the original image in size but also contains the most important information. Without this information, the quality of the reconstructed image will be very poor. Thus, the fingerprinted file is more suitable for distribution in P2P networks, because, in the distribution stage, the uniquely fingerprinted matrix will only be dispensed by the source host and leave the rest for P2P networks to handle. On the other hand, among other frames of the same video which are not decomposed, some will be embedded with sharable fingerprints. The relationship between unique fingerprint and sharable fingerprint and the purpose of using it will be discussed in the paper. Our result indicates that the proposed fingerprint has shown strong robustness against common attacks such as Gaussian noise, median filter, and lossy compression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".