# Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios

arXiv: https://arxiv.org/abs/2602.00109 · HTML: https://mlsanity.com/paper/2602.00109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [3D High-Fidelity Mask Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2108.06968/) (2021)
- [A large-scale study of performance and equity of commercial remote identity verification technologies across demographics](https://mlsanity.com/paper/2409.12318/) (2024)

## Abstract (arXiv)

Presentation attack detection (PAD) subsystems are an important part of effective and user-friendly remote identity validation (RIV) systems. However, ensuring robust performance across diverse environmental and procedural conditions remains a critical challenge. This paper investigates the impact of low-light conditions and automated image acquisition on the robustness of commercial PAD systems using a scenario test of RIV. Our results show that PAD systems experience a significant decline in performance when utilized in low-light or auto-capture scenarios, with a model-predicted increase in error rates by a factor of about four under low-light conditions and a doubling of those odds under auto-capture workflows. Specifically, only one of the tested systems was robust to these perturbations, maintaining a maximum bona fide presentation classification error rate below 3% across all scenarios. Our findings emphasize the importance of testing across diverse environments to ensure robust and reliable PAD performance in real-world applications.