Abstract: Face anti-spoofing aims at detecting whether the input is a real photo of a user (living) or a fake (spoofing) image. As new types of attacks keep emerging, the detection of unknown attacks, known as Zero-Shot Face Anti-Spoofing (ZSFA), has become increasingly important in both academia and industry. Existing ZSFA methods mainly focus on extracting discriminative features between spoofing and living faces. However, the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings, therefore the deceptive features between the known attacks and the livings, which have been ignored by existing ZSFA methods, are essential to comprehensively represent the livings. Therefore, existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged attacks. To tackle this problem, we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing faces. Our method consists of two main components: a two-against-all training strategy and a semantic autoencoder. The two-against-all training strategy is employed to separate deceptive and discriminative features. To address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features, we introduce a modified semantic autoencoder. This autoencoder is designed to map all extracted features to a semantic space, thereby achieving a balance in the dominance of each feature dimension. We combine our method with the feature extraction model ResNet50, and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known spoofing. Experimental results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known and unknown spoofing types.