用少样本学习实现跨国家身份证活体攻击检测，仅需少量样本即可泛化。
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries
- 基于原型网络与元学习，用少量样本训练新国家识别能力。
- 仅需5个身份、每国不足100张图像，即可达到良好检测性能。
- 适用于远程验证系统中未知国家的身份证攻击检测场景。
本文提出一种少样本学习（Few-shot Learning, FSL）方法，用于远程验证系统中身份证活体攻击的检测，并拓展至新国家场景。研究以西班牙和智利文档为基准，评估原型网络在阿根廷和哥斯达黎加等新国家上的泛化能力。重点针对屏幕显示类活体攻击挑战，通过卷积架构与原型网络的元学习机制，构建出在极少样本下仍具高效率的模型。实验表明，仅需5个唯一身份及每国少于100张图像，即可实现优异检测效果。该研究为未知攻击类型下的新型通用身份证活体攻击检测提供了新思路。
原文摘要 · Abstract (English)
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.
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