\section{Conclusions and Discussions}
This work presents an ID layout representation model by adapting a DINOv3 Vision Transformer foundation model to the ID domain through context-aware self-supervised fine-tuning, and supervised discriminative learning with a composite loss formulation. The embedding space is well-suited for downstream tasks including classification, retrieval, clustering, and open-set recognition. Experiments show that the model can classify real world ID layouts with high accuracy.

Fraudulent IDs originating from a common fabrication source exhibit consistent visual artifacts, and form coherent structures in the learned embedding space. Our model makes authentic layouts compact while allowing coherent fraud sources to emerge as off-manifold clusters or high-similarity subgraphs, and proves effective in discovering previously unseen fraud families and scaled fraud campaigns in an adversarial and non-stationary fraud settings. By incorporating embedding similarity into a graph-based detection framework, we can propagate signals from a confirmed fraudulent ID to a broader cluster of related fraud cases. 

The proposed embedding framework provides a production-aligned foundation for human-in-the-loop fraud discovery. While this work focuses on identity documents, the approach naturally generalizes to other semi-structured documents, such as bank statements, pay stubs, tax forms, and certificates, where layout consistency and subtle structural deviations are critical for authenticity verification.