# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Binary Masks  
Compared to depth map supervision, binary mask labels are easier to generate. With binary mask labels, we can determine whether a PA occurs in the corresponding patch, which is attack-type agnostic, spatially interpretable, and easier to generalize to all PAs. The flowchart of this type of method is shown in Figure 7.  
[PERSON] et al. [90] trained the model using both binary and deep pixel-wise supervision, relying solely on frame-level information. As a result, the approach attained low computational and time overhead. However, due to the limited amount of training data available, the method suffered from a weak generalization capability. [PERSON] et al. [91] introduced a learnable attention module to refine features, mitigating the limitations of noisy binary mask labels, especially in partial attacks. [PERSON] et al. [92] demonstrated that local label supervision outperformed global labels for small datasets, introducing ternary  
Figure 6: The flowchart of FAS based on depth maps.  
labels (real, spoofed, and uncertain background) to improve pixel-level predictions. Other researchers, based on the difference in facial material-related reflectance between live skin and spoof media, proposed using both depth and reflection labels to supervise depth models [93; 94]. [PERSON] et al. [93] leveraged material differences between real skin and spoof media, using bilateral convolutional networks (BCNs) with depth, reflection [95], and patch maps for supervision. Despite significant progress in facial depth auxiliary supervision, there remain considerable challenges in modelling lightweight temporal networks. [PERSON] et al. [96] proposed a novel pyramid supervision, guiding depth models to learn local details and global semantics from multiscale spatial contexts. Further advancements incorporated auxiliary supervision from Fourier maps [97], LBP texture maps [98], and sparse 3D point clouds [99], enhancing textural and geometric discrimination. Table 6 summarizes these FAS methods based on binary mask and extended supervision.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Main Idea**} & \multirow{2}{*}{**Supervision**} & \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Attacks**} & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} \\  & & & & & **P** & & **2D-R** & & **3D-M** & **OA** \\ \hline Dual CNN method & \multirow{4}{*}{Depth map} & Replay-Attack & \(\surd\) & \(\surd\) & & & & \\  & combining global and & & CASIA-MFSD & \(\surd\) & \(\surd\) & & & & \\  & local features of face. & & MSU-USRA & \(\surd\) & \(\surd\) & & & & \\  & CDCN can capture & & Replay-Attack & \(\surd\) & \(\surd\) & & & & \\  & detailed patterns via & & CASIA-MFSD & \(\surd\) & \(\surd\) & & & & \\  & aggregating intensity & & & & & & & & \\  & and gradient & & OULU-NPU & \(\surd\) & \(\surd\) & & & & \\  & information, and the & & MSU-MFSD & \(\surd\) & \(\surd\) & & & & \\  & MAFM module obtain & SiW & \(\surd\) & \(\surd\) & & & & \\  & affine-grained features. & & SiW-M & \(\surd\) & \(\surd\) & \(\surd\) & & & \\  & An efficient & & Replay-Attack & \(\surd\) & \(\surd\) & & & face; reduce artifacts & or nameguin \\  & static-dynamic & & CASIA-MFSD & \(\surd\) & \(\surd\) & & & & \\  & representation for & & OULU-NPU & \(\surd\) & \(\surd\) & & & & \\  & & MSU-MFSD & \(\surd\) & \(\surd\) & & & & \\  & spatio-temporal & & SiW-M & \(\surd\) & \(\surd\) & \(\surd\) & & \\  & discrepancy and & & CaSIA-SURF & & & & & & \\  & Meta-NAS for & & & & & & & \\  & robust searching. & & MBU-MV2 & & & & & & \\  & A dual-stream & & Replay-Attack & \(\surd\) & \(\surd\) & & & \\  & spatial-temporal network & & & CASIA-MFSD & \(\surd\) & \(\surd\) & & & \\  & & depth map & OULU-NPU & \(\surd\) & \(\surd\) & & & \\  & multiscale information. & & SiW & \(\surd\) & \(\surd\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of methods for FAS based on depth map.  
Figure 7: The flowchart of FAS based on binary mask.