# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 7 Discussion  
### Algorithmic Efficiency and Speed Performance Comparisons  
* As seen from Tables 2-4, Naive Bayes is the fastest algorithm, taking only approximately 0.03 s. It outperforms all other classifiers by at least an order of magnitude while still delivering excellent average accuracies of 0.89, 0.91, and 0.97 for the StyleGAN3, ProGAN, and InterfaceGAN datasets, respectively. * Logistic Regression is two to three times slower than Naive Bayes; however, it yields better results, particularly when applied to wavelet transforms. * K-Nearest Neighbors (KNNs) is not a good choice of classifier for our datasets and algorithmic pipeline. Despite taking seven to ten times longer to compute than Logistic Regression, it generally performs a little worse. Only in the case of the ProGAN-Fourier transform does it perform marginally better. * For a boost in accuracy compared to the classifiers discussed so far, one could use Gradient Boosting, provided one is willing to wait nearly half a minute on average. For comparison, Gradient Boosting is about one thousand times slower than the Naive Bayes classifier, and offers anywhere from seven to twelve percentage points increase in accuracy over the latter, bearing in mind that Naive Bayes already has at least 87% accuracy. AdaBoost takes only one third the time that Gradient Boosting takes but shows both better and worse dataset- and transform-dependent performance. The more efficient alternative to Gradient Boosting for similar accuracies is XGBoost, which is thirty to one hundred times faster. * Random Forest takes over five seconds on average to execute but underperforms compared to Logistic Regression. Extra Trees takes only one-tenth the time that Random Forest takes, but performs nearly on par or better. However, it still cannot beat Logistic Regression in efficiency for nearly comparable results. * Support Vector Machine (SVM) is ten to fifty times faster than Gradient Boosting and demonstrates better performance for Fourier and wavelet transformations in \begin{table}
\begin{tabular}{c 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 Bayes Log. Regression & 0.9211 & 0.9246 & 0.9209 & 0.9578 & 0.959 & 0.9578 & 0.9888 & 0.9889 & 0.9888 \\  & 0.9847 & 0.9847 & 0.9847 & 1 & 1 & 1 & 1 & 1 \\  & 0.9825 & 0.9826 & 0.9825 & 0.9903 & 0.9903 & 0.9903 & 0.9986 & 0.9986 & 0.9986 \\  & Grad & 0.9947 & 0.9947 & 0.9996 & 0.9996 & 0.9996 & 1 & 1 & 1 \\  & 0.9894 & 0.9894 & 0.9894 & 0.9979 & 0.9979 & 0.9979 & 1 & 1 & 1 \\  & 0.9954 & 0.9954 & 0.9954 & 0.9991 & 0.9991 & 0.9991 & 1 & 1 & 1 \\  & 0.969 & 0.9693 & 0.969 & 0.9845 & 0.9848 & 0.9845 & 0.9948 & 0.9948 & 0.9948 \\  & Extra Trees & 0.9765 & 0.9766 & 0.9765 & 0.9839 & 0.9842 & 0.9839 & 0.9963 & 0.9963 & 0.9963 \\  & SVM & 0.9916 & 0.9916 & 0.9997 & 0.9997 & 0.9997 & 0.9999 & 0.9999 & 0.9999 \\  & & & & & & & & & \\ with RBF & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\  & Kernel & & & & & & & & \\ Neural Network & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\  & 0.9741 & 0.9748 & 0.9741 & 0.5055 & 0.5055 & 0.5047 & 0.9978 & 0.9978 & 0.9978 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: InterfaceGAN results for recall, precision, and F1 score. the StyleGAN3 and ProGAN datasets. It is only marginally worse for the other combinations of dataset and transforms. But SVM cannot always outperform more efficient boosting algorithms such as XGBoost. * SVM with RBF kernel, Gradient Boosting, and AdaBoost are the three slowest classifiers. SVM with RBF kernel takes seven minutes to classify StyleGAN3 and ProGAN datasets, and eleven minutes for InterfaceGAN. * Neural Networks and fine-tuned SVMs consistently achieve close to hundred percent accuracy.