Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition Systems

In this paper, we use synthetic data and propose a new method for template inversion attacks against face recognition systems. We use synthetic data to train a face reconstruction model to generate high-resolution (i.e., $1024\times 1024$ ) face images from facial templates. To this end, we use a face generator network to generate synthetic face images and extract their facial templates using the face recognition model as our training set. Then, we use the synthesized dataset to learn a mapping from facial templates to the intermediate latent space of the same face generator network. We propose our method for both whitebox and blackbox TI attacks. Our experiments show that the trained model with synthetic data can be used to reconstruct face images from templates extracted from real face images. In our experiments, we compare our method with previous methods in the literature in attacks against different state-of-the-art face recognition models on four different face datasets, including the MOBIO, LFW, AgeDB, and IJB-C datasets, demonstrating the effectiveness of our proposed method on real face recognition datasets. Experimental results show our method outperforms previous methods on high-resolution 2D face reconstruction from facial templates and achieve competitive results with SOTA face reconstruction methods. Furthermore, we conduct practical presentation attacks using the generated face images in digital replay attacks against real face recognition systems, showing the vulnerability of face recognition systems to presentation attacks based on our TI attack (with synthetic train data) on real face datasets.