Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users. First, we introduce a new labeling technique, called pseudo-discretized label encoding for generating discretized labels indicating the amount of information related to real images. Secondly, we suggest the expected liveness score based on a regression network for training the difference between the proposed supervision and the expected liveness score. Finally, extensive experiments were conducted on four face anti-spoofing benchmarks to verify our proposed method on both intra-and cross-dataset tests. The experimental results show our approach outperforms previous methods.

Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper, we propose ‘ProtoFL’, Prototypical Representation Distillation based unsupervised Federated Learning to enhance the representation power of a global model and reduce round communication costs. Additionally, we introduce a local one-class classifier based on normalizing flows to improve performance with limited data. Our study represents the first investigation of using FL to improve one-class classification performance. We conduct extensive experiments on five widely used benchmarks, namely MNIST, CIFAR-10, CIFAR-100, ImageNet-30, and Keystroke-Dynamics, to demonstrate the superior performance of our proposed framework over previous methods in the literature.

Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on face anti-spoofing (FAS) against realistic presentation attacks. Recently, various attempts have been made to improve face recognition performance using both global and local learning on face images; however, to the best of our knowledge, this is the first study to investigate whether the robustness of FAS against domain shifts is improved by considering global information and local cues in face images captured using self-attention and convolutional layers. This study proposes a convolutional vision transformer-based framework that achieves robust performance for various unseen domain data. Our model resulted in 7.3%p and 12.9%p increases in FAS performance compared to models using only a convolutional neural network or vision transformer, respectively. It also shows the highest average rank in sub-protocols of cross-dataset setting over the other nine benchmark models for domain generalization.