\begin{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.

    \begin{figure}[h]
        \centering
        \includegraphics[width=0.5\textwidth]{imgs/tsne_CA_clustered_fraud_2d.pdf}
        \caption{2-D t-SNE visualization of Canadian ID embeddings from real-world data. Although the model was trained only on a U.S. ID dataset, the clustering structure demonstrates strong transferability to previously unseen ID layouts. Three anomalous clusters in the center-left correspond to printed fake passports, illustrating how the embedding space can enable large-scale fraud discovery.}
        \label{fig:fig1}
    \end{figure}
\end{abstract}