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.

翻译：人脸形变攻击检测因高质量逼真形变攻击生成技术的进步而日益成为一项具有挑战性的问题。由于此类攻击专门针对边境管控应用，可靠检测形变攻击至关重要。本文提出了一种用于差异形变攻击检测（D-MAD）的多光谱框架。D-MAD方法基于使用从电子护照（也称为参考图像）和可信设备（例如自动边境管控（ABC）闸机）捕获的两张人脸图像，来检测电子护照中呈现的人脸图像是否经过形变。所提出的多光谱D-MAD框架引入作为可信捕获的多光谱图像，通过获取七个不同光谱波段来检测形变攻击。我们在新创建的多光谱形变数据集（MSMD）上开展了广泛实验，该数据集包含143名独特数据对象，在多个会话中使用可见光相机与多光谱相机采集。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性能。