The potential of using the forensic profiles of Australian fraudulent identity documents to assist intelligence-led policing
Bibliographic record
Abstract
The manufacture and distribution of fraudulent identity documents (IDs) is a pervasive and prolific crime problem, enabling the activities of organized crime networks and terrorist cells. As reactive policing methods are ill-equipped to handle the transversal and repetitive nature of document fraud, in 2012 Baechler et al. suggested a complementary method that uses the systematic profiling and comparison of fraudulent IDs to identify those produced by the same source. While this method has been successful in Europe, it is yet to be implemented worldwide, and there is currently little known about the Australian fraudulent document climate. In this pilot study, 43 fraudulent IDs from Sydney-based New South Wales police stations were examined. Adapting the method used in Europe, these documents were imaged, and their visual characteristics were extracted before being organized into an excel database and manually compared. The characteristics chosen are fundamentally linked to the manufacturing process, including the printing methods and replication of security features. Of the documents examined 88% were linked to at least one other document, and five series emerged. These results suggest that the Australian document market may be structured, and that there may be prolific offenders operating at its core, much like in Europe.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".