测试发现光照弱或自动拍摄会令身份验证系统误判率飙升四倍。
Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios
- 在真实场景中测试商业活体检测系统鲁棒性
- 低光下误判率升4倍，自动拍摄下翻倍
- 仅1款系统在所有场景保持误差低于3%
活体攻击检测（PAD）是远程身份验证（RIV）系统的关键组件。然而，在多样环境与流程条件下保证稳定性能仍是重大挑战。本文通过真实场景测试，研究了低光照条件和自动图像采集对商用PAD系统鲁棒性的影响。结果表明，当在低光照或自动捕获场景下使用时，PAD系统的性能显著下降：低光条件下模型预测的误判率增加约四倍，自动采集流程下误判概率翻倍。具体而言，仅有1款被测系统对这些扰动具有鲁棒性，在所有场景下的真实活体误分类误差率均低于3%。研究强调，必须在多样化环境中测试以确保实际应用中的可靠性能。
原文摘要 · Abstract (English)
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.
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