Arc2Morph：基于Arc2Face的身份保持面部变形
Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face
AI总结：
Arc2Morph基于Arc2Face提出的新方法，通过深度学习技术在面部变形攻击检测中实现了与传统关键点方法相当的性能，有效保持身份信息。
摘要：
面部变形攻击被广泛认为是电子身份文档中用于身份验证的面部识别系统中最具挑战性的威胁之一。这些攻击利用了许多国家采用的护照注册程序中的关键漏洞，其中面部图像通常在没有监督的活体捕捉过程中获取。在本文中，我们提出了一种基于Arc2Face的新面部变形技术，Arc2Face是一种能够从紧凑的身份表示中合成逼真面部图像的身份条件面部基础模型。我们通过将所提出的方法在两个大规模隔离面部变形攻击检测数据集上与几种最先进的变形方法进行比较，以及在从FEI和ONOT衍生出的两个新的变形面部数据集上，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在变形攻击潜力上与基于关键点的方法相当，后者通常被视为最具挑战性的。这些发现证实了所提出的方法在变形生成过程中有效保持和管理身份信息的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
