Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios

TL;DR

This study evaluates how environmental factors like low-light and auto-capture affect the robustness of presentation attack detection systems in remote identity validation, highlighting significant performance declines under challenging conditions.

Contribution

It provides empirical evidence on the vulnerability of commercial PAD systems to environmental perturbations and underscores the need for comprehensive testing across diverse scenarios.

Findings

Performance drops by a factor of four in low-light conditions

Auto-capture scenarios double error rates

Only one system maintained error rates below 3% across scenarios

Abstract

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.