Design of new fingerprinting codes using optical orthogonal codes
Bibliographic record
Abstract
Digital fingerprinting has been proposed to restrict illegal distribution of digital media, where every piece of media has a unique fingerprint as an identifying feature that can be traceable. However, fingerprint systems are vulnerable when multiple users collude to create a forged copy by combining their ones. The collusion is modeled as a linear averaging attack, where multiple weighted copies are averaged and the Gaussian noise is then added to the averaged copy. In this paper, a new fingerprint design robust to collusion is proposed, which is to accommodate a lot more users than other existing fingerprint designs. A base matrix is constructed by cyclic shifts of binary sequences in an optical orthogonal code and then extended by a Hadamard matrix. Finally, each column of the resulting matrix will be used as a fingerprint. The focused detection is used to determine whether a user is innocent or guilty in a collusion of linear averaging attack. Simulation results show that the performance of our new fingerprint design is comparable to that of orthogonal and simplex fingerprints.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".