SynID: Passport Synthetic Dataset for Presentation Attack Detection

Christoph Busch; Fabian Stockhardt; Juan E. Tapia; L\'azaro Janier Gonz\'alez-Soler

SynID: Passport Synthetic Dataset for Presentation Attack Detection

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arxiv 2505.07540 v1 pith:OVIOXWAQ submitted 2025-05-12 cs.CV

Juan E. Tapia , Fabian Stockhardt , Lázaro Janier González-Soler , Christoph Busch This is my paper · ORCID

classification cs.CV

keywords documentssyntheticattackdatasetdetectionimagesnumberpassport

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The demand for Presentation Attack Detection (PAD) to identify fraudulent ID documents in remote verification systems has significantly risen in recent years. This increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images. Additionally, we have noticed a surge in the number of attacks aimed at the enrolment process. Training a PAD to detect fake ID documents is very challenging because of the limited number of ID documents available due to privacy concerns. This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information using the ICAO requirement to obtain realistic training and testing images.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?
cs.CV 2025-06 reject novelty 4.0 of 10

DinoV2 features improve ID card presentation attack detection under leave-one-out protocols, and the paper argues that representative bona fide images, not attack diversity, are what enable generalization.

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