Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries

TL;DR

This paper introduces a few-shot learning approach using Prototypical Networks to detect presentation attacks on ID cards across different countries, demonstrating high effectiveness with minimal data for new regions.

Contribution

It extends ID card attack detection to new countries using few-shot learning, showing that effective generalization is possible with limited examples.

Findings

High detection accuracy with as few as five identities.

Effective extension to new countries with under 100 images.

Prototypical Networks outperform baseline methods.

Abstract

This paper proposes a Few-shot Learning (FSL) approach for detecting Presentation Attacks on ID Cards deployed in a remote verification system and its extension to new countries. Our research analyses the performance of Prototypical Networks across documents from Spain and Chile as a baseline and measures the extension of generalisation capabilities of new ID Card countries such as Argentina and Costa Rica. Specifically targeting the challenge of screen display presentation attacks. By leveraging convolutional architectures and meta-learning principles embodied in Prototypical Networks, we have crafted a model that demonstrates high efficacy with Few-shot examples. This research reveals that competitive performance can be achieved with as Few-shots as five unique identities and with under 100 images per new country added. This opens a new insight for novel generalised Presentation Attack Detection on ID cards to unknown attacks.