Bibliographic record
Abstract
This dissertation focuses on digital multimedia content protection from the copyright point of view. Several approaches aiming to resolve the challenge to some extent in the emerging area of multimedia protection were proposed and studied. This study proposes an approach to secure the authorized media sharing in Peer-to-Peer (P2P) networks. The P2P networks was initially designed for bandwidth saving, but its file sharing property was later on put to use for pirate. This situation has not been improved effectively until now. The approach aims to embed an unique-mark (fingerprint) into each authorized copy in the P2P networks so that it can be used to track the pirate initiator. This study also proposes another scheme for protecting the ownership of digital media files that have been circulated without copyright mark embedded. To protect this type of files, the ownership of each file needs to be stored associated with its meta-data (such as the ownership, title and artist) and can be identified correctly later on. Since the size and the number of the media files to be stored are extremely large, the mini versions (fingerprints) of the files become necessary to be derived. The common criteria of designing these two approaches are to ensure the fingerprint is compact, robust, discriminative, and ease of computation. To well balance the criteria, the sparse decomposition techniques play a very important role. The results of the tests under various distortions show the proposed fingerprinting schemes are very promising for real applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".