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

arXiv: https://arxiv.org/abs/2409.06842 · HTML: https://mlsanity.com/paper/2409.06842/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)
- [MIDV-2019: Challenges of the modern mobile-based document OCR](https://mlsanity.com/paper/1910.04009/) (2019)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [Sejong Face Database: A Multi-Modal Disguise Face Database](https://mlsanity.com/paper/2106.07186/) (2021)
- [Single Morphing Attack Detection using Siamese Network and Few-shot Learning](https://mlsanity.com/paper/2206.10969/) (2022)
- [Improving Presentation Attack Detection for ID Cards on Remote Verification Systems](https://mlsanity.com/paper/2301.09542/) (2023)
- [Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style](https://mlsanity.com/paper/2312.13993/) (2023)

## Built on by (most cited first, 5 of 5)

- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)
- [EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents](https://mlsanity.com/paper/2508.16284/) (2025)
- [From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection](https://mlsanity.com/paper/2607.01442/) (2026)
- [Towards Zero-Shot Domain Generalization for ID Cards Presentation Attack Detection](https://mlsanity.com/paper/2608.16591/) (2026)

## Abstract (arXiv)

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.