Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critical vulnerability in passport enrollment procedures adopted by many countries, where the facial image is often acquired without a supervised live capture process. In this paper, we propose a novel face morphing technique based on Arc2Face, an identity-conditioned face foundation model capable of synthesizing photorealistic facial images from compact identity representations. We demonstrate the effectiveness of the proposed approach by comparing the morphing attack potential metric on two large-scale sequestered face morphing attack detection datasets against several state-of-the-art morphing methods, as well as on two novel morphed face datasets derived from FEI and ONOT. Experimental results show that the proposed deep learning-based approach achieves a morphing attack potential comparable to that of landmark-based techniques, which have traditionally been regarded as the most challenging. These findings confirm the ability of the proposed method to effectively preserve and manage identity information during the morph generation process.

翻译：面部融合攻击被广泛认为是电子身份证件所用面部识别系统面临的最具挑战性的威胁之一。此类攻击利用了多国护照签发流程中的一个关键漏洞，即面部图像通常是在无监督的实时采集过程中获取的。本文提出了一种基于Arc2Face的新型面部融合技术，Arc2Face是一种以身份为条件的面部基础模型，能够从紧凑的身份表示中合成逼真的面部图像。我们通过在两个大规模隔离的面部融合攻击检测数据集上，将所提方法的融合攻击潜力指标与多种最先进的融合方法进行比较，并在源自FEI和ONOT的两个新型融合面部数据集上进行评估，从而证明了该方法的有效性。实验结果表明，所提出的基于深度学习的方法达到了与基于特征点技术相当的融合攻击潜力，而后者传统上被认为是最具挑战性的。这些发现证实了所提方法在融合生成过程中能够有效保持和管理身份信息。