通过布局感知表征学习，发现新型身份欺诈并追踪大规模伪造活动。
Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
- 用上下文感知的SimMIM微调DINOv3，结合复合损失提升类别间区分度和类内紧凑性。
- 在加拿大证件上实现99.83%布局分类准确率，识别出276起新欺诈案例，其中222例未被现有检测器发现。
- 支持基于相似性的扩展，从单一已确认案例发现关联欺诈，适用于生产环境下的分布偏移场景。
身份-文档欺诈检测并非静态的二分类问题。自适应攻击者会修改模板与伪造流程，导致历史欺诈标签过时，且成功伪造以协同模式大规模重现。因此，我们研究布局感知表征学习，用于开放集欺诈发现，而非仅闭集分类。通过上下文感知的SimMIM微调与带复合损失的监督度量学习，将DINOv3适配至文档领域，该损失促进类间分离与类内紧凑。模型仅使用美国身份证训练。采用轻量级MLP与Softmax分类器，嵌入在加拿大布局上达到99.83%的布局分类准确率。在包含20,448张加拿大身份证的数据集上，嵌入空间分析揭示276起自适应物理欺诈案例，其中222例未被现有检测器发现。该嵌入支持基于相似性从单一确认种子扩展至未通过传统元数据图关联的其他相关案例。布局感知文档嵌入为应对分布偏移、发现新型及大规模欺诈提供了可落地的基础。
原文摘要 · Abstract (English)
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.
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