## 5 Conclusions  
In this paper, we proposed a system (i.e., DeepDetection) that simultaneously performs user authentication and deep voice detection to prevent voice phishing without privacy leakage. We designed a deep voice detection and user authentication model that achieves  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Deep Voice Detection** & **User Authentication** & **Privacy Preserved** \\ \hline \multirow{2}{*}{This work} & \multirow{2}{*}{O} & Classification & O (by using encoded \\  & & for 20 users & data) \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & \multirow{2}{*}{O} & Real or fake & \multirow{2}{*}{X} \\  & & for 1 user & \\ \hline \multirow{2}{*}{[PERSON] et al. [16]} & \multirow{2}{*}{O} & Real or fake & \multirow{2}{*}{X} \\  & & for 1 user & \\ \hline \multirow{2}{*}{[PERSON] et al. [18]} & \multirow{2}{*}{O} & Real or fake & \multirow{2}{*}{X} \\  & & for 1 user & \\ \hline \multirow{2}{*}{[PERSON] et al. [19]} & \multirow{2}{*}{O} & Real or fake & \multirow{2}{*}{X} \\  & & for 1 user & \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & \multirow{2}{*}{O} & Real or fake & \multirow{2}{*}{X} \\  & & for 1 user & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with previous works. O = provided method and X = not provided method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline Deep Voice & User 1 & User 2 & User 3 & User 4 & User 5 & User 6 & User 7 & User 8 & User 9 \\ \hline \multirow{2}{*}{User 10} & 0.14 & 0.11 & 0.09 & 0.08 & 0.14 & 0.25 & 0.07 & 0.05 & 0.3 \\ \hline \multirow{2}{*}{User 10} & User 11 & User 12 & User 13 & User 14 & User 15 & User 16 & User 17 & User 18 & User 19 & User 20 \\ \hline \multirow{2}{*}{0.08} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.2} & \multirow{2}{*}{0.12} & \multirow{2}{*}{0.04} & \multirow{2}{*}{0.15} & \multirow{2}{*}{0.18} & \multirow{2}{*}{0.12} & \multirow{2}{*}{0.13} & \multirow{2}{*}{0.21} & \multirow{2}{*}{0.35} \\  & & & & & & & & & & \\ \end{tabular}
\end{table}
Table 5: \(EER\) for user authentication in test dataset.  
high performance by preprocessing voice data using an autoencoder. In addition, the autoencoder can be deployed on the user's device for inference. Therefore, the problem of exposing voice data to the server can be prevented. The previous works involved detecting whether a user is a deep voice or an authenticated user, but this work involves a system that can authenticate which of several users he or she is while preserving privacy. To the best of our knowledge, this is the first system that can perform both user authentication and deep voice detection with ASVspoof2019 simultaneously. We achieved an F1 score of 100% in deep voice detection and an F1 score of 99.05% with an average EER of 0.15 in user authentication. This result means that user authentication is possible only when classified with high probability, so the proposed model is robust in determining whether the sender is a deep voice or a correct sender. Therefore, our system can effectively perform security-enhanced user authentication and deep voice detection, which is sufficient to prevent voice phishing. In future works, we will conduct additional research for a more robuste and safer voice phishing prevention system by creating human impersonation data.  
Conceptualization, Y.K.; Software, Y.K., W.K., S.L. and H.K.; Supervision, H.S.; Writing--original draft, Y.K.; Writing--review & editing, W.K., S.L. and H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00264, Research on Blockchain Security Technology for IoT Services, 100%).  
The authors declare no conflict of interest.