2026 |
Pahor, Jure; Peer, Peter; Štruc, Vitomir; Batagelj, Borut Less Learning, More Detection: Identity Document Presentation Attack Detection with Frozen Features Proceedings Article In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2026), pp. 1–9, 2026. Abstract | Links | BibTeX | Tags: forgery detection, identity cards, information forensics, media forensics @inproceedings{LukaIJCB2026,Remote identity verification systems are increasingly exposed to physical presentation attacks (PAs), including print, screen, and composite attacks on identity documents. Detecting such attacks, commonly referred to as identity document presentation attack detection (PAD), is challenging because the relevant forensic cues are often local, subtle, and sensitive to acquisition conditions. Recent PAD methods therefore rely on increasingly sophisticated deep learning pipelines, including task-specific heads, learned patch fusion modules, and backbone adaptation. In this work, we revisit identity document PAD from the perspective of feature reuse and ask whether frozen self-supervised foundation model representations already encode the local forensic cues needed for reliable detection. We propose PADCore, a lightweight patch-based framework that decomposes each document into local patches, embeds them using a frozen DINOv2 (or DINOv3) backbone, constructs compact per-class coresets of representative embeddings, and fits a Linear Discriminant Analysis classifier on top. PADCore requires no backbone fine-tuning and can be efficiently re-fitted as new attack data becomes available. On FakeIDet2-db, PADCore matches or surpasses the state-of-the-art FakeIDet2 approach in in-distribution evaluation and achieves stronger generalization on KID34K, while reducing training time from hours to under four minutes. Its patch-based design is also compatible with privacy-preserving workflows based on pre-extracted anonymized document patches. |