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.
Universal language model fine-tuning for text classification.Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 328–339, 2018
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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.