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. |
Brodarič, Marko; Scheirer, Walter; Jain, Deepak Kumar; Peer, Peter; Štruc, Vitomir Learning Forgery Signals from Synthetic Depth Targets for Generalizable Deepfake Detection Proceedings Article In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2026), pp. 1–10, 2026. Abstract | Links | BibTeX | Tags: deepfake, deepfake DAD, deepfake detection, deepfakes, information forensics, media forensics @inproceedings{BrodaricIJCB2026,Deepfake detection remains challenging under distribution shifts caused by unseen generation pipelines, post-processing, and unconstrained capture conditions. While most existing detectors primarily reason in RGB appearance space, facial manipulations also disturb geometric structure and consistency, making depth an attractive complementary cue. Existing depth-aware methods, however, typically use depth as an auxiliary input or guidance signal and may therefore underutilize the representational richness of modern monocular depth foundation models. To address this limitation, we propose a mechanism that adapts a pretrained monocular depth foundation model into a forgery-aware representation extractor for deepfake detection. Based on the adapted model, we then propose a novel depth-driven detector, termed {FADepth}, that uses the resulting depth representation as the primary source of evidence and complements it with RGB appearance cues. Extensive experiments on six benchmark datasets and with 24 state-of-the-art detectors show that FADepth yields highly competitive performance, achieving an overall mAUC of 88.70. The source code of the model is available at https://github.com/markobrodaric/FADepth |