On the Generation of Face Morphs by Inversion of Optimal Morph Embeddings

Automatic face recognition systems are widely used in different applications which require authentication. Among various types of attacks against face recognition systems, morphing attacks have become a major concern, where face images of two subjects are combined into a face morph image which is submitted for enrolment. In a successful attack, both contributing subjects can then authenticate against the morph reference. In this work, we propose a new method to generate face morphs based on inversion of the optimal morph embeddings. To this end, we first find the optimal morph embeddings using the face embeddings of two source face images and then use state-of-the-art template inversion techniques to generate the morph. We use three different template inversion methods: the first one exploits a fully self-contained embedding-to-image inversion model, while the second and third leverage the realistic image generation of a pretrained StyleGAN network and a foundation model based on diffusion models, respectively. Furthermore, we use optimization methods to improve the performance of template inversion methods in the generation of face morph images from optimal morph embeddings. In our experiments, we evaluate the performance of generated face morph images and compare them with state-of-the-art morph generation methods, showing the superiority of our method. We showcase that our method can outperform state-of-the-art deep-learning-based morph generation methods, both in white-box and black-box attack scenarios, and compete with state-of-the-art landmark-based morph generation methods. Moreover, we perform a practical print-scan attack to simulate a real-world scenario and compare our method with previous methods in the literature, demonstrating the effectiveness and superiority of our method. The source code of our proposed method and all experiments are publicly available.