# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Binary Supervision  
In the early stages of applying DL to FAS, researchers treat the task of distinguishing between real and fake faces as a simple binary classification problem. When training neural networks, the training data is labelled with 0 and 1 to represent fake faces and real faces, respectively, and classification is achieved by calculating the difference between the network's predictions and the 0/1 labels using Binary Cross Entropy (BCE) loss. The flowchart of this type of method is shown in Figure 5.  
[PERSON] et al. [78] proposed a transfer learning method that used the sigmoid function for the binary classification of real and fake faces, achieving state-of-the-art results on the 3 DMAD and Replay-Attack datasets. [PERSON] et al. [79] adjusted Inception-v3 [80], ResNet-50 [81], and ResNet-152 [81] for binary classification, evaluated their performance on the MSU-MFSD dataset, and provided evaluation results and application suggestions for the three models. To address the difficulty of obtaining spoofed face data for DL, [PERSON] et al. [82] first converted 2D printed photos into 3D objects, then simulated operations  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Strategies** & **Main Idea and Ref. ** & **Datasets** & \begin{tabular}{l} **Attacks** \\ **2D-P** \\ \end{tabular}  & \begin{tabular}{l} **3D-P** \\ **2D-M** \\ \end{tabular}  & \begin{tabular}{l} **Advantages** \\ **M** \\ \end{tabular}  & \begin{tabular}{l} **Disadvantages** \\ \end{tabular}  \\ \hline \begin{tabular}{l} Cascade model fusion: \\ Traditionally, handcrafted \\ feature extraction is used as a \\ preprocessing step, followed \\ by further processing of the \\ extracted features using a DL \\ to achieve finer features. \\ \end{tabular}  & \begin{tabular}{l} Dense optical flow-based \\ motion features, shearlet-based \\ image quality features and \\ DNN [60] \\ \end{tabular}  & CASA MPSD & \(\surd\) & \(\surd\) & & \\ \hline \begin{tabular}{l} Cascade model fusion: \\ Traditionally, handcrafted \\ feature extraction is used as a \\ preprocessing step, followed \\ by further processing of the \\ extracted features using a DL \\ to achieve finer features. \\ \end{tabular}  & \begin{tabular}{l} Dense optical flow-based \\ motion features, shearlet-based \\ image quality features and \\ DNN [60] \\ \end{tabular}  & CASA MPSD & \(\surd\) & \(\surd\) & & \\ \hline \begin{tabular}{l} Deep residual \\ pre-trained \\ feature processing of the \\ extracted features using a DL \\ features and texture cues, \\ attention module and CNN [62] \\ \end{tabular}  & \ \begin{tabular}{l} Replay-Attack \\ 3D-MAD \\ \end{tabular}  &
\begin{tabular}{l} \(\surd  
such as bending and rotating the printed photos in 3D space to synthesize a large number of 3D virtual spoofed photos. Finally, they used the synthesized samples to train a modified ResNet-50 and present the binary classification results. On the other hand, considering the weak intra-class and inter-class constraints of BCE loss, some studies modify the BCE loss to provide CNN with more discriminative supervisory signals. [PERSON] et al. [83] reformulated FAS as a fine-grained classification problem instead of a binary classification problem and used type labels (e.g., bonafide, print, and replay) for multi-class supervision. However, FAS models supervised with multi-level CE loss may fail when faced with high-fidelity PAs. To learn a compact space with small intra-class distances and large inter-class distances, [PERSON] et al. [84] introduced contrastive loss and triplet loss. However, unlike visual retrieval tasks, bonafide and PAs in FAS tasks usually have asymmetric internal distributions (more compact and diverse, respectively). Based on this evidence, [PERSON] et al. [85] proposed using asymmetric angular margin softmax loss to supervise FAS patch models, thereby relaxing the intra-class constraints among PAs. Table 4 shows an overview of the methods for FAS based on binary supervision.