There exist two principal categories of conventional face anti-spoofing (FAS) techniques: appearance-based and temporal-based methods. Appearance-based methods involve the extraction of hand-crafted features for classification, such as local binary patterns (LBPs) [9,10] and scale-invariant feature transform (SIFT) [11]. However, temporal-based FAS methods detect attack faces by extracting temporal cues from a sequence of consecutive face frames. Mouth movement detection [12] and eye blink detection [13,14] are examples of the earliest dynamic texture-detection methods. However, these methods do not generalize well to cross-dataset testing scenarios due to the dissimilarities in feature spaces among diverse domains, which often lead to feature bias during generalization.

Recently, deep neural networks, specifically convolutional neural networks (CNNs), have gained widespread adoption in computer vision tasks and have been extensively applied to FAS. Yang et al. [15] were the pioneers in utilizing binary classification CNNs for FAS. Jourabloo et al. [16] proposed a face de-spoofing technique that performs fake face classification by reverse decomposition into real faces and spoof noise. Liu et al. [17] presented a CNN-RNN network that combines both appearance and temporal cues to detect spoof attacks using remote photoplethysmography (rPPG) signals. Similarly, 3D mask detection attack methods [18,19] also exploit rPPG information. Yang et al. [20] combined temporal and appearance cues to detect fake faces from real ones. Roy et al. [21] investigated frame-level FAS to enhance biometric authentication security against face-spoofing attacks. More recently, deep neural networks have been applied to FAS [4,7,8,22,23,24], achieving superior performance compared to conventional methods [15,25,26,27].

In conclusion, traditional FAS methods include appearance-based and temporal-based methods, which extract hand-crafted features and mine temporal cues, respectively. However, deep neural networks, especially CNNs, have achieved state-of-the-art performance in FAS by combining the appearance and temporal cues, using techniques such as reverse decomposition and frame-level FAS for improved biometric authentication security.

The use of multiple datasets has recently sparked research interest in multi-domain processing. In particular, the research community has amassed several large-scale FAS datasets with rich annotations [28,29,30,31]. The work on multi-source domain processing shares similarities with domain adaptation (DA) methods [32,33,34,35,36,37,38,39] that require a retrained model to perform well on both source and target domain data. Specifically, Zhang et al. [37] introduced the concept of margin disparity discrepancy to characterize the differences between source and target domains, which has inspired the researchers' work. Ariza et al. [40] conducted a comparative study of several classification methods, which informed the researchers' experimental design. Additionally, Liu et al. [41] proposed the YOLOv3-FDL model for successful small crack detection from GPR images using a four-scale detection layer. Notably, the common approaches of Mancini et al. [34] and Rebuffi et al. [35] employ the ResNet [42] architecture, which offers benefits over architectures such as VGG [43] and AlexNet [44] by increasing abstraction through convolutional layers. Yang et al. [45] recently enriched FAS datasets from a different perspective to achieve multi-domain training, while Guo et al. [46] proposed a novel multi-domain model that overcomes the forgetting problem when learning new domain samples and exhibits high adaptability.

Domain adaptation (DA) and domain generalization (DG) are two fundamental methods used in FAS research. While the DG method mines the relationships among multiple domains, the DA method aims to adapt the model to a target domain. In this work, the researchers propose a novel domain generalization method that introduces a new loss function inspired by the work of Motiian et al. [47], which encourages feature extraction in similar classes. To align multiple source domains for generalization, previous works such as Ghifary et al. [48] and Li et al. [49] have proposed autoencoder-based approaches. The method follows a similar approach of learning a shared feature space across multiple source domains that can generalize to the target domain. Previous works such as Shao et al. [7], Saha et al. [50], Jia et al. [8], and Kim et al. [51] have also attempted to achieve this goal. Among them, the single-side adversarial learning method proposed in SSDG [8] is the work most related to ours. However, this end-to-end approach overlooks the relationships between different domains. To address the overfitting and generalization problems of adversarial generative networks, Li et al. [5] proposed a multi-channel convolutional neural network (MCCNN). Additionally, meta-learning formulations [52,53,54] have been utilized to simulate the domain shift during training to learn a representative feature space. However, recent works such as Wang et al. [6] have not adopted a domain-alignment approach but have instead increased the diversity of labeled data by reassembling different styles and content features in their SSAN method.