Perception and detection of counterfeit currency in Canada: note quality, training, and security features
Bibliographic record
Abstract
Commissioned by the Bank of Canada to help improve the detection of counterfeit currency, we designed a series of tests of performance to explore the contributions of note quality, sensory modality, training, security features and demographic variables to the accuracy of counterfeit detection with three different note types. In each test, participants (general public, and cash handlers, divided amongst commercial cash handlers and bank tellers) were presented with notes, one at a time, for up to seven seconds, and were asked to judge whether each note was genuine or counterfeit. With whole note inspection, overall accuracy was about 80%. When the security features were tested individually, the Optical Security Device (OSD) was the best feature, the hidden number was the worst, and the portrait, maple leaves, fluorescence, and microprinting were intermediate. Accuracy was higher with notes that could be seen but not touched than vice versa. Cash handlers were 74% correct with touch alone and adding touch to vision significantly improved counterfeit detection. This paper will demonstrate how performance differences between the different note types can be explained in terms of the efficacy of the individual security features incorporated into the notes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".