# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Spoof Trace Separation  
We refer to accurately isolating the essential spoofing features from a fake face as spoof trace separation. This type of FAS algorithm involves three basic steps, as seen in Figure 9. First, we input the fake face \(I^{\prime}\) into the generator G to produce the spoof trace image \(\text{G}\left(\text{I}^{\prime}\right)\). Next, we subtract the spoof trace image \(\text{G}\left(\text{I}^{\prime}\right)\) from the fake face I'm to generate a synthetic real image \(\text{I}^{\prime}-\text{G}\left(\text{I}^{\prime}\right)\). Finally, the final classification result will be computed based on the separated spoof traces. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Main Idea**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Attacks**} & \multirow{2}{*}{**Ab-M**} & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} \\  & & & & & & & & & \\ \hline \multirow{5}{*}{ \begin{tabular}{l} [100] \\ fine-grained facial \\ motion cues \\ \end{tabular}  } & \multirow{5}{*}{Resnet} & \multirow{5}{*}{Replay-Attack} & \multirow{5}{*}{\(\surd\)} & \multirow{5}{*}{\(\surd\)} & \multirow{5}{*}{\(\surd\)} & \multirow{5}{*}{\ \begin{tabular}{l} Contributing to the \\ verification accuracy, the \\ MPEM enhances the \\ recall rate of the \\ attack videos. \\ \end{tabular}  } & \multirow{5}{*}{ \begin{tabular}{l} Local planar \\ homography involving \\ complex calculations \\ \end{tabular}  } \\  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & & & & & & \\ \cline{5-7}  & & & &[PERSON] et al. [106] introduced a three-part CNN structure, DS-Net for separating spoof traces and reconstruct real faces, DQ-Net for estimating depth information, and VQ-Net for differentiating reconstructed real faces from original ones. These networks operated iteratively, improving spoof detection. Similarly, [PERSON] et al. [107] extended on this concept with the spoof trace disentanglement network (STDN), which not only reconstructed real faces but also generated synthetic spoofed faces, offering enhanced interpretability and robustness against diverse attacks. Compared to [106], ref. [107] had stronger interpretability and resistance to diverse attacks. [PERSON] et al. [108] proposed a network with a spoof trace generator and auxiliary classifier, which addressed overfitting and enhanced generalization. [PERSON] et al. [109] utilized a dual spoof disentanglement generation (DSDG) framework with a variational autoencoder (VAE) to disentangle latent features and enhance robustness. Similarly to [98], [PERSON] et al. [110] combined dual-stage learning with reconstruction techniques to separate spoof-related features, improving the recognition of various attacks. [PERSON] et al. [111] addressed domain shift with the DiffFAS framework, which employed generative diffusion models to create high-fidelity spoof faces, thereby enhancing data diversity and generalization. [PERSON] et al. [112] used Fourier frequency disentanglement and data augmentation to improve cross-domain generalization by extracting frequency features and enhancing training samples. FAS algorithms, which utilize spoof trace separation, enhance accuracy and interpretability by disentangling spoof traces from facial features, increasing robustness against complex attacks. However, these algorithms require high computational resources, complex structures, and cumbersome training processes. Therefore, they are best suited for scenarios that require high security levels, such as financial systems, facial payment, and government applications. Table 8 provides a summary of these methods.