# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 5 Experiments
### Performance Evaluation and Comparison of Different Architectures  
We tested the performance of Xception model architecture and Efficient Net model architecture on the augmented DFDC dataset, and evaluated the performance using a confusion matrix. Table 5 shows the confusion matrix of the Efficient Net model. This confusion matrix was created for the unseen DFDC dataset. This matrix shows us the number of True Positive, False Positive, True Negative, and False Negative values. It can be determined from the table that out of all the real images, 8543 images were correctly classified as real images and 1242 images were misclassified as fake images. Similarly, out of all the fake images, 8448 images were correctly classified as fake images whereas 1094 images were misclassified as real images  
Table 6 shows the confusion matrix of the Xception model. This confusion matrix was created for the unseen augmented DFDC dataset. This matrix shows us the number of True Positive, False Positive, True Negative, and False Negative values. It can be determined from the table that out of all the real images, 8792 images were correctly classified as real images and 993 images were misclassified as fake images. Similarly, out of all the fake  
Figure 10: Testing loss and training loss of our proposed model on the augmented dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**N = 19,327** & **Predicted Real** & **Predicted Fake** \\ \hline Actual Real & 8543 & 1242 \\ Actual Fake & 1094 & 8448 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Efficient Net confusion matrix. images, 8620 images were correctly classified as fake images whereas 922 images were misclassified as real images. Table 7 shows the confusion matrix of our proposed model. This confusion matrix was created for the unseen augmented DFDC dataset. This matrix shows us the number of True Positive, False Positive, True Negative, and False Negative values. It can be determined from the table that out of all the real images, 8908 images were correctly classified as real images and 877 images were misclassified as fake images. Similarly, out of all the fake images, 8879 images were correctly classified as fake images whereas 663 images were misclassified as real images  
We experimented with various face detector methods and various deep learning state of the art models combined with different classifiers. The different face detector techniques experimented with were Blaze face, YOLO [33,34], and dlib [35]. Considering that it had the most accurate and fast detection of the faces, we used Blaze face detection technique in our proposed model. Various deep learning models were used on the detected faces, including Xception [36], Efficient Net B5 [37], ResNet152 [38], InceptionResNet152V2 [17], etc., to evaluate their performance on DFDC dataset. Various classifiers like SVM [39], Logistic Regression, XGBoost [35], etc. were applied on top of these networks to classify the video as real or fake. After evaluating and experimenting with various combinations of face detectors, deep learning networks, and classifiers, we found that BlazeFace + the Proposed Model + XGBoost performed best among all these combinations and attained the highest accuracy [40]. We compared the performance of our proposed model architecture with the Xception model architecture and Efficient Net model architecture. We proved that our proposed model architecture had the highest performance among the three. We calculated this performance using the DFDC datasets [41,42]. We measured the performance through precision, recall, F1 score, and accuracy. In addition, we considered log loss and proved that the proposed model attains minimum testing loss and the highest testing accuracy [43]. This shows that the proposed model outperforms the other models for deepfake video detection on the DFDC dataset. Table 8 shows the comparison of the proposed model with the Efficient Net and Xception models relative to Precision, Recall, F1-Score, and Accuracy.