Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.

翻译：面部形态攻击对人脸识别系统构成日益严重的威胁。一张形态合成照片包含两个不同主体的生物特征信息，旨在利用人脸识别系统的漏洞。当形态合成图像经过印刷扫描处理以掩盖合成过程中产生的伪影时，这些系统尤其容易受到攻击。我们通过对异构形态攻击场景的一系列评估，研究了印刷扫描对形态攻击检测的影响。实验结果表明，印刷扫描处理可使配对形态呈现匹配率提升高达8.48%。此外，当单图像形态攻击检测算法未针对印刷扫描形态进行训练时，形态攻击分类错误率可能上升高达96.12%，这表明系统存在显著的安全隐患。