# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 5 Experiments
### Performance Evaluation and Comparison of Different Architectures
From Table 8, it can be seen that results are poor for feature extraction using Efficient Net B5 and classifier performance using SVM. Xception and Efficient Net improved performance by up to 2%. It can also be observed that classification accuracy is low and computation time is high in a deeper network like Resnet152 [12,13]. The proposed DFN along with the YOLO model is better than Efficient Net B5. Moreover, DFN with SVM and DenseLayer achieved satisfactory performance. The highest classification accuracy of 93.28% was obtained using DFN and XGBoost [14]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**N = 19,327** & **Predicted Real** & **Predicted Fake** \\ \hline Actual Real & 8792 & 993 \\ Actual Fake & 922 & 8620 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Xception confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline
**N = 19,327** & **Predicted Real** & **Predicted Fake** \\ \hline Actual Real & 8908 & 877 \\ Actual Fake & 663 & 8879 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Proposed model confusion matrix. Figure 11 shows the comparison of the performance of our proposed model architecture graphically in terms of precision, recall, and accuracy. These results were obtained from the testing data of our dataset. Table 9 compares the performance of our proposed model in terms of training and testing loss and accuracy with the Efficient Net and Xception models [15]. These values  
Figure 11: Performance measures comparison Xception and Efficient Net models with the proposed model [17; 39]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline BlazeFace + Efficient Net B5 [43] + SVM [39] & 0.7985 & 0.8094 & 0.8021 & 0.8246 \\ YOLO + Xception [36] + SVM [39] & 0.8030 & 0.7902 & 0.7853 & 0.8337 \\ MTCNN [31; 41; 43] + InceptionResNetV2 + XGBoost & 0.8265 & 0.8045 & 0.8218 & 0.8445 \\ YOLO + InceptionResNetV2 + XGBoost [YYX] [17] & 0.8736 & 0.8539 & 0.8636 & 0.9073 \\ YOLO + ResNet152 [38] + SVM [39] & 0.7828 & 0.8012 & 0.7878 & 0.8250 \\ YOLO + ResNet152 [38] + XGBoost & 0.8043 & 0.8129 & 0.8083 & 0.8488 \\ BlazeFace + Efficient Net B5 [43] + XGBoost & 0.8285 & 0.8145 & 0.8012 & 0.8458 \\ BlazeFace + Xception [36] + XGBoost & 0.8278 & 0.8945 & 0.8986 & 0.9037 \\ YOLO + Xception [36] + Log Reg & 0.7645 & 0.7724 & 0.7954 & 0.8102 \\ YOLO + Efficient Net B5 [43] + Log Reg & 0.7827 & 0.8152 & 0.8021 & 0.8342 \\ BlazeFace + Efficient Net B5 + XGBoost & 0.8985 & 0.9094 & 0.9021 & 0.9146 \\ YOLO + DFN+ SVM & 0.8503 & 0.8469 & 0.8517 & 0.8528 \\ YOLO + DFN + XGBoost & 0.8627 & 0.8745 & 0.8468 & 0.8762 \\ BlazeFace + DFN+ SVM & 0.8971 & 0.9069 & 0.8823 & 0.9028 \\ BlazeFace + DFN + DenseLayer [36; 37; 38] & 0.8192 & 0.8363 & 0.8241 & 0.8152 \\ BlazeFace + DFN + Log Reg & 0.9078 & 0.9186 & 0.9254 & 0.9105 \\ Proposed: BlazeFace + DFN+ XGBoost & 0.9103 & 0.9269 & 0.9217 & 0.9328 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance Comparison of Proposed Method with Other Methods Using the DFDC Dataset. were obtained by training and testing all three models on the augmented data set [44; 45]. We found that the Efficient Net model performs better than the Xception model in terms of both training and testing accuracy, and has less loss. Our model performs best among these models.