# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 5 Research Challenges and Future Research Directions  
### FAS-Based Generalization  
Challenge: From the analysis of FAS methods in Section 4, it is evident that several effective methods have been proposed within the realm of generalization research. These include adversarial domain adaptation, generative domain adaptation, meta-learning, style alignment, gradient alignment, and various deep representation learning techniques. These methods have significantly improved the performance of FAS in cross-domain scenarios. Moreover, zero-shot learning, few-shot learning, and anomaly detection show promise in handling unknown attacks. These traditional domain adaptation methods typically require simultaneous access to source and target domain data. However, source domain data may not be shared in real-world scenarios due to privacy concerns, security concerns, or other reasons. Therefore, data privacy and security issues need further exploration and resolution in the field of FAS.  
Future Research Directions: (1) Source-free domain adaptation (SFDA): SFDA has become a research hotspot in recent years, aiming to perform domain adaptation when source domain data is unavailable. SFDA methods adapt to the target domain using only pre-trained source models and target domain data. [PERSON] et al. [126] combined domain-generalized pretraining with SFDA to enhance the cross-domain generalization ability of FAS models. Some studies have introduced federated learning [211] and prompt learning [136, 212] into the DA scenario. For example, [PERSON] et al. [136] introduced class-free prompt learning (CFPL) to improve the cross-domain generalization of FAS. Therefore, future research could explore methods integrating federated learning, prompt learning, and DG techniques for domain adaptation. (2) Multi-domain adaptation: Future research could explore adapting to multiple target domains and effectively transferring knowledge  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Datasets** & **Year** & **Attack Types** & **Modal** & **\#Sub** &
\begin{tabular}{c} **\#Live/** \\ **Spoof** \\ \end{tabular} & **Setup** \\ \hline \multirow{7}{*}{7} & WMAC & \multirow{7}{*}{2019} & Print (flat, Repplay (tablelet), & \multirow{7}{*}{1000} & \multirow{7}{*}{72} & Six sessions with different \\  & WMAC & & Partial (glasses), Mask & RGB, & & backgrounds and \\  & & & (plastic, & Depth, & 72 & 347/1332 (V) & illumination; \\  & & & silicone, and & NIR, Therma & & & pulse data for bornding \\  & & & paper, Mannequin) & & & & recordings \\ \multirow{8}{*}{8} & CASIA-SURF & \multirow{8}{*}{2019} & \multirow{8}{*}{Print (flat, wrapped, cut)} & RGB, & \multirow{8}{*}{1000} & \multirow{8}{*}{3000/ (V)} & Background removed; \\  & & & & Depth, & & 18,000 (V) & Randomly cut eyes, nose or \\  & & & NIR & & & mouth areas & \\  & CASIA-SURF & \multirow{8}{*}{2021} & \multirow{8}{*}{Print (flat, wrapped), Repplay,} & RGB, & \multirow{8}{*}{1607} & \multirow{8}{*}{300/ (V)} & Three ethnicities; outdoor \& \\  & CeFA & & Mask (3D print, silica gel) & & & & 27,900 (V) & decoration with \\  & & & & NIR & & & & wig and glasses \\ \multirow{10}{*}{10} & PADIS-Faco & \multirow{8}{*}{2021} & Print (flat), Repplay (tablelet, & \multirow{8}{*}{360} & \multirow{8}{*}{1105/ (V)} & \multirow{8}{*}{60-frame sequence of} \\  & & & phone), Partial (glasses, & RGB, Depth, & & & \\  & & & & NIR, SWIR, & & 924 (V) & 60-frame sequence of \\  & & & silicone, & Thermal & & & 1984 \(\times\) 1264 pixel images \\  & & & transparent, Mannequin & & & & & Hyprespectrical images were \\  & & & & & & & & \\  & & & & & & &simultaneously. Multi-domain adaptation can help models perform consistently across different devices and environments, improving their generalization capability.