# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Depth Maps  
In the field of computer vision, a depth map is an image or image channel that contains information about the distance from the surface of the target object to the viewpoint. When applied to FAS, the depth map highlights the differences in distance between the surface of real and spoofed faces and the camera. For real faces, which have a three-dimensional structure, there are significant distance variations between different regions (such as the nose and ears) and the camera. In contrast, spoofed faces are typically planar, resulting in little to no variation in the distances between different regions of the spoofed face and the camera. The specific training process is illustrated in Figure 6. Initially, the depth maps corresponding to real or spoofed face images are used as supervisory signals to guide the model in predicting the depth map of the input image. Subsequently, a depth supervision loss is employed to make the final classification decision. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Main Idea** & **Supervision** & **Datasets** & & \multicolumn{3}{c}{**Attacks**} & & \\  & & & **2D-P** & **2D-R** & **3D-M** & **OA** & **Advantage** & **Disadvantage** \\ \hline \([\)\(\)\(\mathcal{F}\)\(]\) & Transfer learning and CNN & BCE & Replay-Attack & \(\surd\) & \(\surd\) & & & & \\  & Analysis of effectiveness & BCE & MSU-MFSD & \(\surd\) & \(\surd\) & & & & \\  & on CNN & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \([\)\(\mathcal{F}\)\(]\) & Transfer learning and CNN & BCE & Replay-Attack & \(\surd\) & \(\surd\) & & & & & \\  & Analysis of effectiveness & BCE & MSU-MFSD & \(\surd\) & \(\surd\) & & & & \\  & on CNN & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of methods for FAS based on binary supervision. [PERSON] et al. [86] proposed a dual CNN method combining global and local facial features. First, one CNN was used to obtain a depth map of the image globally (i.e., spatial cues) and provide a \"live\" score based on this information. Then, another CNN learned distinguishable texture features from randomly selected small local patches of the image and provided another score. Finally, these two scores were fused to obtain the final decision, enhancing the accuracy of the judgment. However, FAS methods face two significant challenges: extracting dynamic features from multi-frame image sequences increases the model's time complexity, and expert-designed network structures struggle to capture fine-grained information in images and quickly adapt to different environments. To enhance the automatic design and fine-grained depth feature acquisition capabilities, [PERSON] et al. [87] first introduced neural architecture search (NAS) into FAS to automatically find the optimal backbone architecture for capturing central difference convolution (CDC). They also designed multiscale attention fusion (MAFM) to refine and fuse low, mid, and high-level CDC features through spatial attention. On this basis, they extend CDCN to a more automated and better-performing CDCN++. To ensure NAS for FAS generalize well to new environments and new types of attacks, [PERSON] et al. [88] further developed CDC and pooling operators, exploited an efficient static-dynamic representation for mining the FAS-aware spatio-temporal discrepancy, and proposed domain/type-aware Meta-NAS for robust searching. [PERSON] et al. [89] designed a dual-stream spatial-temporal network to explore potential depth and multiscale information. In this network, a temporal shift module was introduced to extract temporal information, and symmetric loss was used for more accurate auxiliary supervision in depth information estimation network.