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 improve the ability of the morphing attack to represent characteristics from both identities. We demonstrate the high fidelity of the proposed attack by evaluating its visual fidelity via the Frechet Inception Distance. Extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The proposed attack is compared to two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. The ability of a morphing attack detector to detect the proposed attack is measured and compared against the other attacks. Additionally, a novel metric to measure the relative strength between morphing attacks is introduced and evaluated.

翻译：变形攻击的成败取决于变形攻击是否能够代表两种身份的生物鉴别特征。我们展示了一种新型变形攻击,即由两种不同身份的生物鉴别特征构成的变形图像,目的是用两种身份中的一个身份引起虚假的接受,从而对生物鉴别系统构成重大威胁。变形攻击的成功取决于变形图像能否代表两种身份的生物鉴别特征的特征,而这两种身份用来制作图像。我们展示了一种新颖的变形攻击,这种变形攻击使用了一种基于变形的建筑来提高图像的视觉真实性,并提高变形攻击能够代表两种身份的特征。我们通过Frechet Inpeption距离评估其视觉真实性,显示了拟议攻击的高度忠诚性。进行了广泛的实验,以衡量变形攻击系统对拟议攻击的脆弱性。拟议攻击与两次基于Landmark的攻击相比,采用了两种最先进的以GAN为基础的变形攻击。对变形攻击探测拟议攻击的能力进行了测量,并与其他攻击相比较。此外,还评估了一种衡量变形攻击之间相对强度的新指标。