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.
Cite
@article{arxiv.2602.00109,
title = {Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios},
author = {John J. Howard and Richard O. Plesh and Yevgeniy B. Sirotin and Jerry L. Tipton and Arun R. Vemury},
journal= {arXiv preprint arXiv:2602.00109},
year = {2026}
}
Comments
Accepted to the IEEE/CVF WACV 2026 Workshop on Generative, Adversarial and Presentation Attacks in Biometrics (GAPBio). 8 pages, 6 figures, 4 tables