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.

本文提出了一种多光谱框架用于差异化仿真攻击检测，该方法通过在电子护照中捕获的基准图像和在可信设备中捕获的面部图像来检测电子护照中呈现的人脸图像是否被修改，并在多个会话中使用可见光和多光谱相机对 143 个独特数据主体进行了广泛实验，结果表明该多光谱框架的性能优于可见光图像检测。