Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a significant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image and the ability of the morphing attack to represent characteristics from both identities. We demonstrate the effectiveness of the proposed attack by evaluating its visual fidelity via the Frechet Inception Distance (FID). Also, extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The ability of a morphing attack detector to detect the proposed attack is measured and compared against two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. Additionally, a novel metric to measure the relative strength between different morphing attacks is introduced and evaluated.

翻译：人脸变形攻击旨在通过呈现融合了两种不同身份生物特征信息的变形图像，欺骗人脸识别系统（FR），使其与其中任一身份产生错误匹配，从而对生物识别系统构成重大威胁。变形攻击的成功与否，取决于变形图像能否有效表征用于生成该图像的两个身份的生物特征。本文提出一种基于扩散模型架构的新型变形攻击方法，可提升图像的视觉保真度，并增强变形攻击对双身份特征的表征能力。通过弗雷歇初始距离（FID）评估视觉保真度，验证了所提攻击的有效性。同时开展大量实验，衡量人脸识别系统对该攻击的脆弱性。将变形攻击检测器对本攻击的检测能力，与两种基于生成对抗网络（GAN）的主流变形攻击及两种基于特征点的攻击进行对比评估。此外，本文引入并验证了用于衡量不同变形攻击相对强度的新指标。