测试近红外与可见光人脸匹配系统对伪造攻击的脆弱性
The Invisible Threat: Evaluating the Vulnerability of Cross-Spectral Face Recognition to Presentation Attacks
- 对比近红外与可见光图像进行人脸识别时的攻防表现
- 发现该系统仍易受特定伪造攻击，可靠性不足
- 适合关注跨谱人脸识别安全的研究者阅读
跨谱人脸识别系统旨在通过在复杂条件下实现跨模态匹配来提升人脸识别性能。一个关键应用是将近红外（NIR）图像与可见光（VIS）图像进行匹配，从而利用VIS参考图像验证通过NIR采集的面部。使用NIR成像具有诸多优势，包括对光照变化更具鲁棒性、透过眼镜和眩光能力更强，以及对呈现攻击更具抵抗力。尽管有这些宣称的优势，文献中尚未系统研究基于NIR的系统对呈现攻击的鲁棒性。本文对NIR-VIS跨谱人脸识别系统在呈现攻击下的脆弱性进行了全面评估。实证结果表明，尽管此类系统表现出一定可靠性，但仍易受到特定攻击，凸显了该领域进一步研究的必要性。
原文摘要 · Abstract (English)
Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational conditions. A particularly relevant application is the matching of near-infrared (NIR) images to visible-spectrum (VIS) images, enabling the verification of individuals by comparing NIR facial captures acquired with VIS reference images. The use of NIR imaging offers several advantages, including greater robustness to illumination variations, better visibility through glasses and glare, and greater resistance to presentation attacks. Despite these claimed benefits, the robustness of NIR-based systems against presentation attacks has not been systematically studied in the literature. In this work, we conduct a comprehensive evaluation into the vulnerability of NIR-VIS cross-spectral face recognition systems to presentation attacks. Our empirical findings indicate that, although these systems exhibit a certain degree of reliability, they remain vulnerable to specific attacks, emphasizing the need for further research in this area.
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