The potential of using the forensic profiles of Australian fraudulent identity documents to assist intelligence-led policing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it