# SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
## 4 Experiment
### Deepfake Scenario Experiment  
In this section, we construct a training scenario for a deepfake detection model in the real world and train the model. The scenario involves training a deepfake model with first-generation deepfake data, then experimenting with the detection of first-generation deepfakes (NT) that were not participated while training, and post-generation (second-generation) deepfakes (StyleGAN) that are newly developed and unknown. Table 12 shows the training results of various models according to the scenario. As a result of the experiment, among various models, the SupCon-MPL model achieved highest performance in all scenario evaluations compared with other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Train** \\ **Dataset** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Unlabeled** \\ **Dataset** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Creterained** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **SupCon** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **Model** \\ \end{tabular} } \\  & & & **ACC** & **AUC** & **F1** \\  & & & & **Score** & **AUC** & **Score** & **AUC** & **AUC** & **AUC** & **Score** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} ResNet50 \\ (without FS) \\ \end{tabular} } & FF & DF (unknown) & 64.24 & 64.27 & **65.49** & 62.88 & 62.84 & 58.55 & **64.60** & **64.55** & 61.60 \\  & FF & F2F + FS + NT (known) & 70.56 & 71.36 & 83.36 & 75.44 & 75.52 & 83.01 & **75.84** & **76.00** & **83.50** \\  & FF & F2F (unknown) & 55.76 & 55.64 & 40.63 & 56.61 & 56.64 & 49.47 & **58.74** & **58.77** & **51.52** \\  & FF & DF + FS + NT (known) & 77.26 & 76.89 & 81.66 & 75.54 & 75.54 & 84.13 & **78.11** & **78.28** & **85.84** \\  & FF & FS (unknown) & 54.47 & 52.07 & 79.18 & **55.75** & **53.68** & 77.20 & 55.72 & 53.41 & **79.29** \\  & FF & DF + F2F + NT (known) & 75.99 & 75.87 & 81.66 & 76.02 & 75.88 & 84.84 & **77.22** & **77.12** & **85.84** \\  & FF & NT (unknown) & 56.71 & **54.95** & 62.66 & 56.58 & 54.31 & 66.27 & **56.76** & 54.02 & **69.43** \\  & DF + F2F + FS (known) & 77.39 & 77.41 & 81.66 & 79.09 & 79.26 & 84.13 & **81.22** & **81.32** & **85.84** \\ \hline \hline \end{tabular}
\end{table}
Table 11: The performance of SupCon-MPL compared with the baseline model and SupCon model [35].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Scenario Deepfakes** \\ **(Known)** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Current-Generation** \\ **Deepfakes** \\ **(Unknown)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Post-Generation** \\ **Deepfakes** \\ **(Unknown)** \\ \end{tabular} } \\  & & 52.40 & 44.62 & 49.96 \\ DDT [31] & 80.41 & 44.62 & 49.49 \\ MPL [24] & 79.82 & 56.53 & 43.16 \\ SupCon [35] & 79.01 & 56.66 & 47.77 \\ SupCon-MPL (ours) & **81.40** & **57.85** & **51.90** \\ \hline \hline \end{tabular}
\end{table}
Table 12: The performance of SupCon-MPL compared with other deepfake detection methods.