# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 6 Results
### Recall, Precision, F1 Score  
In this section, we report the recall, precision, and F1 score for each classifier. Table 5, Table 6, and Table 7 report the recall, precision, and F1 score for StyleGAN3, ProGAN, and InterfaceGAN, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Transform** & \multicolumn{2}{c}{**Fourier**} & \multicolumn{2}{c}{**Wavelet**} & \multicolumn{2}{c}{**HOG**} \\ \hline
**Classifier** & **Accuracy** & **Time (in sec)** & **Accuracy** & **Time (in sec)** & **Accuracy** & **Time (in sec)** \\ \hline Naive Bayes & 0.9211 & 0.03 & 0.9578 & 0.03 & 0.9888 & 0.03 \\ Log. & 0.9847 & 0.11 & 1 & 0.08 & 1 & 0.06 \\ KNN & 0.9825 & 0.87 & 0.9903 & 0.76 & 0.9986 & 0.78 \\ Grad Boost & 0.9947 & 33.45 & 0.9996 & 32.95 & 1 & 33.55 \\ AdaBoost & 0.9894 & 10.19 & 0.9979 & 10.23 & 1 & 10.09 \\ XGBoost & 0.9954 & 1.75 & 0.9991 & 0.38 & 1 & 1.54 \\ Random & 0.969 & 5.54 & 0.9845 & 5.4 & 0.9948 & 5.45 \\ Extra Trees & 0.9765 & 0.63 & 0.9839 & 0.64 & 0.9963 & 0.62 \\ SVM & 0.9916 & 1.21 & 0.9997 & 1.3 & 0.9999 & 0.54 \\ RBF Kernel & 1 & 670.29 & 1 & 681.27 & 1 & 660.09 \\ NeuralNetwork & 1 & 7.8 & 1 & 7.32 & 1 & 7.55 \\ GMM & 0.9741 & 0.72 & 0.5055 & 1.97 & 0.9978 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: InterfaceGAN. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Transform** & \multicolumn{2}{c}{**Fourier**} & \multicolumn{2}{c}{**Wavelet**} & \multicolumn{2}{c}{**HOG**} \\ \hline
**Classifier** & **Accuracy** & **Time (in sec)** & **Accuracy** & **Time (in sec)** & **Accuracy** & **Time (in sec)** \\ \hline Naive Bayes & 0.8817 & 0.03 & 0.909 & 0.04 & 0.969 & 0.04 \\ Log. & 0.8975 & 0.1 & 0.9885 & 0.12 & 0.9991 & 0.07 \\ Regression & 0.9131 & 0.88 & 0.9716 & 0.76 & 0.9979 & 0.74 \\ KNN & 0.9483 & 33.43 & 0.996 & 33.25 & 0.9998 & 33.6 \\ AdaBoost & 0.9021 & 10.13 & 0.9827 & 10.18 & 1 & 10.21 \\ XGBoost & 0.9453 & 1.96 & 0.9931 & 0.34 & 0.9999 & 0.33 \\ Random & 0.882 & 5.43 & 0.974 & 5.38 & 0.9841 & 5.51 \\ Extra Trees & 0.9066 & 0.66 & 0.9803 & 0.66 & 0.9942 & 0.67 \\ SVM & 0.9525 & 3.95 & 0.9975 & 1.89 & 0.9997 & 0.74 \\ SVM with & 1 & 439.38 & 1 & 441.86 & 1 & 439.51 \\ RBF Kernel & 0.9929 & 6.8 & 0.9989 & 8.04 & 1 & 7.48 \\ GMM & 0.181 & 1.14 & 0.4806 & 2.45 & 0.997 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ProGAN. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Transform** & \multicolumn{3}{c}{**Fourier**} & \multicolumn{3}{c}{**Wavelet**} & \multicolumn{3}{c}{**HOG**} \\ \hline
**Classifier** & **Recall** & **Precision** & **F1 Score** & **Recall** & **Precision** & **F1 Score** & **Recall** & **Precision** & **F1 Score** \\ \hline Naive & 0.8817 & 0.8825 & 0.8816 & 0.909 & 0.909 & 0.909 & 0.969 & 0.9695 & 0.969 \\ Bayes & Log. Regression & 0.8975 & 0.8975 & 0.8975 & 0.9885 & 0.9885 & 0.9981 & 0.9991 & 0.9991 \\ KNN & 0.9131 & 0.9143 & 0.913 & 0.9716 & 0.9716 & 0.9716 & 0.9979 & 0.9979 \\ Grad & 0.9483 & 0.9489 & 0.9483 & 0.996 & 0.996 & 0.9998 & 0.9998 & 0.9998 \\ AdaBoost & 0.9021 & 0.9023 & 0.9021 & 0.9827 & 0.9827 & 0.9827 & 1 & 1 & 1 \\ XGBoost & 0.9453 & 0.946 & 0.9453 & 0.9931 & 0.9931 & 0.9931 & 0.9999 & 0.9999 & 0.9999 \\ Random & 0.882 & 0.8875 & 0.8816 & 0.974 & 0.974 & 0.974 & 0.9841 & 0.9843 & 0.9841 \\ Extra & 0.9066 & 0.9083 & 0.9065 & 0.9803 & 0.9803 & 0.9803 & 0.9942 & 0.9942 & 0.9942 \\ SVM & 0.9525 & 0.9527 & 0.9525 & 0.9975 & 0.9975 & 0.9975 & 0.9997 & 0.9997 & 0.9997 \\ SVM & & & & & & & & \\ with RBF & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Neural & 0.9929 & 0.9929 & 0.9929 & 0.9989 & 0.9989 & 0.9989 & 1 & 1 & 1 \\ GMM & 0.181 & 0.1372 & 0.1555 & 0.4806 & 0.4805 & 0.4799 & 0.997 & 0.997 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ProGAN results for recall, precision, and F1 score.