# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 1 Introduction
### Motivation and Contribution
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** &
\begin{tabular}{c} **Ref.** \\ **Count** \\ \end{tabular} & **Topics and Observation** & **Disadvantages** & **Our Article** \\ \hline
[16] & 2019 & 208 & Various indicators of FLD & \
\begin{tabular}{c} It does not cover DL-based \\ FAS algorithms. \\ \end{tabular} &
\begin{tabular}{c} We provide a systematic \\ introduction to \\ DL-based methods. \\ \end{tabular} \\
[17] & 2020 & 57 & FAS methods on 3D masks &
\begin{tabular}{c} The number of DL-based \\ algorithms was very small. \\ Did not address the \\ generalization of FAS \\ methods. \\ \end{tabular} \\
[18] & 2020 & 58 & \begin{tabular}{c} The application of CNN in \\ face and iris attack detection \\ DL-based methods and FAS \\ on multi-modal fusion \\ Face presentation attacks and \\ detection based on domain (DA) \\ \end{tabular} & \
\begin{tabular}{c} The application of CNN in \\ face and iris attack detection \\ methods. \\ Sufficient discussion on \\ generalization. \\ \end{tabular} &
\begin{tabular}{c} The provided a detailed \\ introduction to the \\ cross-domain FAS, including \\ DA, domain generalization \\ (DG), zero-/few-shot \\ learning- and anomaly detection. \\ \end{tabular} \\
[21] & 2021 & 115 & \begin{tabular}{c} A literature review on face \\ adversarial attacks and defenses \\ \end{tabular} & \
\begin{tabular}{c} Lack of a consistent \\ evaluation framework, and \\ omission of \\ experimental datasets. \\ \end{tabular} &
\begin{tabular}{c} Our review summarizes \\ detection methods for various \\ types of presentation attacks, \\ including adversarial attacks. \\ \end{tabular} \\
[10] & 2022 & 253 & \
\begin{tabular}{c} A comprehensive review on \\ FAS based on DL \\ \end{tabular} &
[15] & 2022 & 252 & \begin{tabular}{c} A comprehensive review on \\ FAS based on DL \\ \end{tabular} & \
\begin{tabular}{c} The discussion of \\ cross-domain FAS issues \\ requires a more in-depth \\ examination, and the analysis \\ of FAS on smartphones \\ \end{tabular} &
\begin{tabular}{c} We include not only \\ DL-based methods but also \\ FAS methods \\ combining with handcrafted \\ and DL approaches, as well \\ as smartphone-based \\ detection methods. \\ \end{tabular} \\
[23] & 2023 & 15 & \
\begin{tabular}{c} A survey on FAS techniques \\ for smartphones \\ \end{tabular} &
\begin{tabular}{c} Standardized evaluation and \\ insufficient exploration of \\ future directions. \\ \end{tabular} \\
[24] & 2023 & 275 & \begin{tabular}{c} A survey on DG methods \\ across various \\ application domains \\ \end{tabular} & \
\begin{tabular}{c} Although the review includes \\ a summary of methods for \\ FAS, it needs a \\ systematic approach. \\ \end{tabular} &
\begin{tabular}{c} We provide a detailed \\ introduction to the \\ cross-domain generalization \\ of FAS \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the previous literature reviews. presentation attacks in Section 3. We discuss the experimental datasets in Section 4. Furthermore, we discuss current challenges and potential future research directions in Section 5. At the end, Section 6 is the conclusion.