Adapting DINOv3 via SimMIM and composite metric learning on U.S. IDs yields 99.83% Canadian layout accuracy and surfaces 276 fraud cases (222 missed by prior detectors) in 20k Canadian IDs via embedding analysis.
An efficient method to detect series of fraudulent identity documents based on digitised forensic data.Science & Justice, 62(5):610–620
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Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
Adapting DINOv3 via SimMIM and composite metric learning on U.S. IDs yields 99.83% Canadian layout accuracy and surfaces 276 fraud cases (222 missed by prior detectors) in 20k Canadian IDs via embedding analysis.