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 capture seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created datasets 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.

翻译：人脸变形攻击检测因高质量逼真变形攻击生成技术的进步正成为日益严峻的挑战。可靠的变形攻击检测对于边境控制应用至关重要。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方法基于使用从电子护照（亦称参考图像）和可信设备（例如自动边境管制闸机）捕获的两张人脸图像，以检测电子护照中呈现的人脸图像是否被变形。所提出的多光谱D-MAD框架引入多光谱图像作为可信捕获，通过捕获七个不同光谱波段来检测变形攻击。在包含143名独特数据对象的新建数据集上进行了大量实验，这些数据对象在多个会话中使用可见光和多光谱相机捕获。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。