[2605.05215] Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05215 (cs)
[Submitted on 17 Apr 2026]
Title:Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
Authors:Jinxing Li, Nicholas Ren, Cathy Chang, Hongkai Pan, Daniel George
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Abstract: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% layout classification accuracy on Canadian layouts. Moreover, on a dataset of 20,448 Canadian IDs, embedding-space analysis surfaces 276 adaptive physical-fraud cases, including 222 not surfaced by incumbent detectors. The embedding supports similarity-based expansion from a single confirmed seed to additional related cases not linked by conventional metadata graphs. The layout-aware document embeddings provide a production-aligned basis for discovering novel and campaign-scale fraud under distribution shift.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.05215 [cs.CV]
(or arXiv:2605.05215v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05215
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arXiv-issued DOI via DataCite
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From: Jinxing Li [view email] [v1] Fri, 17 Apr 2026 01:20:40 UTC (5,964 KB)
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