Group-Oriented Fingerprinting for Multimedia Forensics
Bibliographic record
Abstract
Digital fingerprinting of multimedia data involves embedding information in the content signal and offers protection to the digital rights of the content by allowing illegitimate usage of the content to be identified by authorized parties. One potential threat to fingerprinting is collusion, whereby a group of adversaries combine their individual copies in an attempt to remove the underlying fingerprints. Former studies indicate that collusion attacks based on a few dozen independent copies can confound a fingerprinting system that employs orthogonal modulation. However, in practice an adversary is more likely to collude with some users than with other users due to geographic or social circumstances. To take advantage of prior knowledge of the collusion pattern, we propose a two-tier group-oriented fingerprinting scheme where users likely to collude with each other are assigned correlated fingerprints. Additionally, we extend our construction to represent the natural social and geographic hierarchical relationships between users by developing a more flexible tree-structure-based fingerprinting system. We also propose a multistage colluder identification scheme by taking advantage of the hierarchial nature of the fingerprints. We evaluate the performance of the proposed fingerprinting scheme by studying the collusion resistance of a fingerprinting system employing Gaussian-distributed fingerprints. Our results show that the group-oriented fingerprinting system provides the superior collusion resistance over a system employing orthogonal modulation when knowledge of the potential collusion pattern is available.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".