# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### Domain generalization  
Domain generalization (DG) [24; 128] aims to train a model on source domains that can generalize to unseen target domains, without access to target domain data during training. Unlike DA, where target domain data is available, DG focuses on learning a model that minimizes prediction error in the unknown target domain. DG is more challenging because target domain data is unavailable and the model must be robust against out-of-distribution scenarios. Most DG research focuses on domain-invariant representation learning, using techniques like feature alignment, style alignment, gradient alignment, and meta-learning to identify representations that are effective across different domains. The schematic diagram of the model is illustrated in Figure 10b. **Feature alignment**: [PERSON] et al. [129] developed generalized feature extractors for multiple source domains through adversarial training, supplemented by auxiliary deep supervision to enhance generalization. They applied dual-force triple mining constraints to achieve compact within-class and dispersed between-class results. [PERSON] et al. [130] argued that narrowing the distribution of attacks across domains might result in the neglect of domain-specific information. Consequently, they used adversarial learning and inequality three-tuple loss to treat attacks and live faces as separate categories. [PERSON] et al. [131] utilized GANs to transfer images from RGB to the depth domain, addressing challenges like lighting and device variations. [PERSON] et al. [132] introduced adaptive normalized representation learning (ANRL) to select domain-agnostic features adaptively, improving generalization with dual calibrated constraints (DCC) to aggregate the multiple source samples of the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Main MAE**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Datasets**} & \multicolumn{5}{c}{**Attacks**} \\  & & & & & **2D-F** & **2D-R** & **3D-M** & **OA** & **Advantages** & **Dosed-sensing** \\ \hline \multirow{11}{*}{[155]} & MML, Adversarialflower Spears, & & & & & & & & & \\  & and learned mean & & & & & & & & & \\  & matching (QKD) & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of FAS methods based on domain adaptation. same class. [PERSON] et al. [133] focused on camera-invariant spoofing features through high-frequency feature decomposition and enhanced discriminative capability by combining reconstructed images with high and low-frequency information. [PERSON] et al. [134] applied consistency regularization to improve generalization across different environments, thereby reducing overfitting. [PERSON] et al. [135] introduced a domain-invariant ViT (DiVT) for FAS that used a concentration loss to learn domain-invariant representations and a separation loss to distinguish attack types across domains. However, ViT tends to capture noisy features from background objects and other irrelevant details. [PERSON] et al. [136] developed CFPL-FAS, which employed class-free prompt learning (CFPL) to learn different semantic prompts based on content and style features separately, with the aim of enhancing the model's performance on unseen domains. [PERSON] et al. [137] proposed VL-FAS, using fine-grained natural language descriptions and a sample-level visual text optimization (SLVT) module for improving domain generalization, though it did not fully leverage language supervision. [PERSON] et al. [138] introduced TF-FAS, which combined material and depth features with a semantic guidance mechanism for better cross-domain generalization. **Style alignment:** [PERSON] et al. [139] introduced the shuffled style assembly network (SSAN) to separate content and style in feature representations, creating a stylized feature space.