Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
Identity-document fraud detection is not a stationary binary classification problem. Adaptive attackers modify templates and fabrication pipelines, making historical fraud labels stale, and successful forgeries recur at scale as coherent campaigns. We therefore study layout-aware representation learning for open-set fraud discovery rather than only closed-set classification. We adapt DINOv3 to the document domain via context-aware SimMIM fine-tuning and supervised metric learning with composite loss that encourages inter-class separability and intra-class compactness. The model is trained with U.S. IDs only. With a lightweight MLP and softmax classifier, the embedding achieves 99.83% layo...
| Authors | Jinxing Li, Nicholas Ren, Cathy Chang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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