用于差分人脸变形攻击检测的多光谱成像：一项初步研究
Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study
AI总结：
本文提出一种基于多光谱成像的差分人脸变形攻击检测（D-MAD）框架，通过获取七个光谱带作为可信捕获图像，在包含143名对象的MSMD数据集上验证了其优于可见光图像的检测性能。
摘要：
由于高质量且逼真的变形攻击生成技术的进步，人脸变形攻击检测正成为一个日益具有挑战性的问题。可靠地检测变形攻击至关重要，因为这些攻击主要针对边境控制应用。本文提出了一种用于差分变形攻击检测（D-MAD）的多光谱框架。D-MAD方法基于使用两张分别从电子护照（也称为参考图像）和可信设备（例如自动边境控制（ABC）闸机）捕获的人脸图像，来检测电子护照中呈现的人脸图像是否被变形处理。所提出的多光谱D-MAD框架引入了作为可信捕获的多光谱图像，获取七个不同的光谱带来检测变形攻击。在新建的多光谱变形数据集（MSMD）上进行了大量实验，该数据集包含143名独立数据对象，这些对象是在多个会话中使用可见光和多光谱相机捕获的。结果表明，与可见光图像相比，所提出的多光谱框架具有更优越的性能。
英文摘要：
Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multispectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
