# SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
## 4 Experiment
### Single-Domain Experiment  
In this section, single-domain experiments evaluate the detection performance for a single deepfake generation model, while, in Section 4.3, the performance on new deepfakes is evaluated through training on various deepfake generation models. Sections 4.4 and 4.5 assess the performance of deepfake detection across diverse deepfake datasets and scenario-based deepfake detection, respectively. In the experiment using only one domain, as shown in Table 1, the performance of the known domain increased in most of the situation. Furthermore, it was confirmed that the performance in unknown domains also increased in most of the situation. Based on EfficientNet-b5 [21] in Table 1, ACC and AUC in K improved by an average of 4.47% and 4.53%, respectively, and ACC in U improved by an average of 0.20%, but AUC decreased by an average of 0.13%. However, overall, out of a total of 64 ACC and AUC validations recorded in Tables 1-8, 44 and 41 cases improved, respectively. Through this, it was confirmed that when using the same data, the performance of the MPL model is higher than the pretrained model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** & **Train** & **Test** & \multicolumn{3}{c}{**Pretrained Model**} & \multicolumn{3}{c}{**MPL Model**} \\
**Model** & **Dataset** & **Dataset** & **ACC** & **AUC** & **F1 Score** & **ACC** & **AUC** & **F1 Score** \\ \hline \multirow{4}{*}{EfficientNetb5 [21]} & DF & DF & 89.35 & 89.35 & 89.19 & **90.08** & **90.08** & **90.36** \\  & F2F & F2F & 77.21 & 77.21 & 77.61 & **80.35** & **80.35** & **79.45** \\  & FS & FS & 84.52 & 84.31 & 83.20 & **87.90** & **87.43** & **85.98** \\  & NT & NT & 64.13 & 63.33 & 58.18 & **74.79** & **74.47** & **71.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of pretrained and MPL model on the known domain (EfficientNetb5 [21]). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** & **Train** & **Test** & \multicolumn{3}{c}{**Pretrained Model**} & \multicolumn{3}{c}{**MPL Model**} \\
**Model** & **Dataset** & **Dataset** & **ACC** & **AUC** & **F1 Score** & **ACC** & **AUC** & **F1 Score** \\ \hline \multirow{4}{*}{EfficientNetb5 [21]} & DF & DF & 89.35 & 89.35 & 89.19 & **90.08** & **90.08** & **90.36** \\  & F2F & F2F & 77.21 & 77.21 & 77.61 & **80.35** & **80.35** & **79.45** \\  & FS & FS & 84.52 & 84.31 & 83.20 & **87.90** & **87.43** & **85.98** \\  & NT & NT & 64.13 & 63.33 & 58.18 & **74.79** & **74.47** & **71.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of pretrained and MPL model on the unknown domain (EfficientNetb5 [21]). \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}{*}{ResNet101 [50]} & DF & DF & **91.16** & **91.16** & 90.84 & 91.13 & 91.13 & **90.85** \\  & F2F & F2F & 81.41 & 81.41 & 80.27 & **83.50** & **83.49** & **82.73** \\  & FS & FS & 87.59 & 87.37 & 86.13 & **87.75** & **87.66** & **86.39** \\  & NT & NT & 74.17 & 74.56 & 73.16 & **76.03** & **75.53** & **73.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of pretrained and MPL model on the unknown domain (ResNet101 [50]).