AI RESEARCH
Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
arXiv CS.LG
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ArXi:2605.05215v1 Announce Type: cross 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.