# SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
## 4 Experiment
### Single-Domain Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Baseline** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Train** \\ **Dataset** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **Pretrained Model** \\ **ACC** \\ \end{tabular} & \multicolumn{4}{c}{**MPL Model**} \\  & & & **ACC** & **AUC** & **F1 Score** & **ACC** & **AUC** & **F1 Score** \\ \hline \multirow{8}{*}{ResNet50 [50]} & DF & F2F & **52.91** & **52.89** & **22.41** & 51.92 & 51.86 & 20.36 \\  & DF & FS & 56.80 & 52.68 & 21.41 & **57.02** & **53.00** & **22.63** \\  & NT & **55.91** & **51.77** & **18.54** & 55.45 & 51.33 & 18.53 \\ \cline{2-10}  & \multirow{4}{*}{F2F} & DF & **53.94** & **53.90** & **31.72** & 53.79 & 53.75 & 28.28 \\  & FS & FS & 54.62 & 50.97 & **24.47** & **55.32** & **51.35** & 21.84 \\  & & NT & 54.76 & **51.20** & **24.07** & **54.96** & 51.04 & 21.00 \\ \cline{2-10}  & \multirow{4}{*}{FS} & DF & 56.34 & 56.30 & 33.89 & **59.57** & **59.53** & **41.50** \\  & FS & F2F & 51.16 & 51.10 & 20.08 & **51.57** & **51.52** & **21.01** \\  & & NT & 53.67 & 49.44 & 14.32 & **53.81** & **49.61** & **15.49** \\ \cline{2-10}  & \multirow{4}{*}{NT} & DF & **60.45** & **60.43** & 49.93 & 60.17 & 60.15 & **50.85** \\  & & F2F & **54.74** & **54.70** & **38.88** & 53.56 & 53.51 & 36.77 \\ \cline{2-10}  & & FS & 51.59 & 48.15 & 22.30 & **51.84** & **48.51** & **25.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of pretrained and MPL model on the unknown domain (ResNet50 [50]). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Baseline** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Train** \\ **Dataset** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **Pretrained Model** \\ **ACC** \\ \end{tabular} & \multicolumn{4}{c}{**MPL Model**} \\  & & & **ACC** & **AUC** & **F1 Score** & **ACC** & **AUC** & **F1 Score** \\ \hline \multirow{8}{*}{ResNet50 [50]} & DF & DF & 91.16 & 91.16 & 90.91 & **91.29** & **91.30** & **90.97** \\  & F2F & F2F & 82.47 & 82.48 & 81.84 & **83.98** & **83.97** & **82.75** \\  & FS & FS & 87.55 & 87.21 & 86.01 & **88.31** & **88.09** & **87.06** \\  & NT & NT & 74.96 & 74.28 & 71.20 & **76.22** & **75.84** & **73.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of pretrained and MPL model on the known domain (ResNet50 [50]). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Baseline** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Train** \\ **Dataset** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **Pretrained Model** \\ **ACC** \\ \end{tabular} & \multicolumn{4}{c}{**MPL Model**} \\  & & & **ACC** & **AUC** & **AUC** & **AUC** & **F1 Score** \\ \hline \multirow{8}{*}{ResNet101 [50]} & DF & DF & **52.69** & **52.63** & **21.22** & 51.26 & 51.20 & 18.01 \\  & DF & FS & **56.90** & **52.73** & **20.42** & 56.02 & 51.84 & 18.71 \\  & NT & **55.93** & **51.74** & **18.33** & 54.93 & 50.73 & 16.53 \\ \cline{2-10}  & \multirow{4}{*}{F2F} & DF & **53.63** & **53.59** & **30.44** & 53.50 & 53.46 & 27.42 \\  & FS & FS & 54.33 & 50.08 & **22.82** & **54.86** & **50.85** & 19.67 \\  & NT & 54.62 & 51.01 & **23.30** & **55.17** & **51.28** & 20.98 \\ \cline{2-10}  & \multirow{4}{*}{FS} & DF & 57.04 & 57.70 & 37.00 & **59.55** & **59.51** & **42.55** \\  & F2F & 51.43 & 51.37 & 20.63 & **51.84** & **51.79** & **24.60** \\ \cline{2-10}  & & NT & **53.66** & 49.55 & 15.04 & 53.61 & **49.59** & **17.59** \\ \cline{2-10}  & \multirow{4}{*}{NT} & DF & **62.66** & **62.65** & **59.08** & 60.16 & 60.14 & 49.99 \\ \cline{2-10}  & & F2F & **52.69** & **52.63** & **47.55** & 51.26 & 51.20 & 37.97 \\ \cline{2-10}  & & FS & 56.90 & 52.73 & **29.98** & **56.02** & **51.84** & 21.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of pretrained and MPL model on the known domain (ResNet101 [50]).