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Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?
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Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?
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Nowadays, one of the main challenges in presentation attack detection (PAD) on ID cards is obtaining generalisation capabilities for a diversity of countries that are issuing ID cards. Most PAD systems are trained on one, two, or three ID documents because of privacy protection concerns. As a result, they do not obtain competitive results for commercial purposes when tested in an unknown new ID card country. In this scenario, Foundation Models (FM) trained on huge datasets can help to improve generalisation capabilities. This work intends to improve and benchmark the capabilities of FM and how to use them to adapt the generalisation on PAD of ID Documents. Different test protocols were used, considering zero-shot and fine-tuning and two different ID card datasets. One private dataset based on Chilean IDs and one open-set based on three ID countries: Finland, Spain, and Slovakia. Our findings indicate that bona fide images are the key to generalisation.
Forward citations
Cited by 2 Pith papers
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From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection
A systematic survey unifies presentation, digital injection, and GenAI synthesis attacks on identity documents, audits datasets for a reality gap, identifies SDGI in multimodal models, and reports APCER above 25% for ...
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From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards
A compact multimodal vision-text model for cross-domain ID card presentation attack detection generalizes well after fine-tuning but fails zero-shot, showing synthetic datasets may not capture real-world challenges.
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