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：人脸融合攻击旨在通过呈现一张融合图像来欺骗人脸识别系统，该图像包含来自两个不同身份的生物特征质量，其目的是触发与其中一个身份的误通过，从而对生物识别系统构成重大威胁。融合攻击的成功取决于融合图像代表创建该图像所用两个身份生物特征的能力。我们提出了一种基于扩散架构的新型融合攻击，以提升图像的视觉保真度以及融合攻击代表两个身份特征的能力。我们通过使用Fréchet Inception Distance (FID) 评估其视觉保真度，证明了所提攻击的有效性。此外，我们进行了大量实验来衡量人脸识别系统对该攻击的脆弱性。我们测量了融合攻击检测器检测所提攻击的能力，并将其与两种基于GAN的最新融合攻击以及两种基于地标的攻击进行了比较。另外，我们引入并评估了一种用于衡量不同融合攻击之间相对强度的新型指标。