## 4 Experiments
### Performance Evaluation  
Figure 8 illustrates the ROC curve related to the processing of the validation set, the good performance of the classifier can also be seen, in which the curve is pushed to the upper left corner of the graphic. This means that the area under the ROC is also higher. Figure 9 illustrates the confusion matrix of our model with the Celeb-DF v2 dataset. Our model is compared to the current state-of-the-art model [39]. The model's performance is equivalent to SOTA. We used the train dataset to train our model and the validation set to test it. In Figure 10, the confusion matrix of our model is compared to that of the SOTA model [39]. Our model has a relatively comparable 0.958 of AUC and 0.9243 of F1-score to the SOTA model, which had an AUC of 0.972 and 0.906. However, since SOTA is built on Efficient Network, which is often very heavy, our proposed method is better in terms of a lighter architecture while maintaining the same level of quality. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy of Validation Set** & **Loss of Validation Set** \\ \hline Celeb-DF v2 & 94.2\% & 0.3 \\ \hline DFDC & 93.75\% & 0.295 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training results of the classifier network on validation set of Celeb-DF v2 and DFDC. Figure 6: The training output of classifier network on the Celeb-DF v2 dataset. The accuracy of the model is on the left, while the loss of the model is on the right. The red represents the training outcomes, while the blue represents the validation outcomes. Figure 7: The training output of classifier network on the DFDC dataset. The accuracy of the model is on the left, while the loss of the model is on the right. The red represents the training outcomes, while the blue represents the validation outcomes. The comparison of AUC values is shown in Table 3. For Celeb-DF v2 dataset, the majority of researches in [5, 6, 47, 53, 54] may not have a high AUC. Outstanding research can be mentioned is [PERSON] et al. [28], which achieved a pretty good result of 90.56% AUC with this dataset. Not only that, but their outcomes are also superior in terms of a number of parameters and their research goal is to create a lightweight model that can operate on systems with minimal hardware requirements. In this section, our method achieved an AUC of 97.8%, which is somewhat slightly better than theirs. Our fundamental model is based on a deep neural network for classification; thus, the number of parameters is frequently larger. For DFDC dataset, the best results can be mentioned in the research of [PERSON] et al. [4] with 97.8% AUC. On the other hand, the idea of Young-Jin research is similar to SOTA model [39], they are also based on the Vision Transformer model, EfficientNet with distillation methodology. Thus, their results must have a large number of parameters although they are not mentioned in their research contents. In this section our results are a  
Figure 8: ROC-AUC curve. The left side is the curve of Celeb-DF v2 validation set, the right side is the curve of DFDC validation set. Figure 10: Confusion Matrix with DFDC dataset [19]; the left side is the SOTA model’s prediction which is described in [PERSON] et al. [4] results and the right side is our results. Top-right, top-left, bottom-right, bottom-left means false-positive, true-negative, true-positive, false-negative in order. Figure 9: Confusion Matrix of our model with the Celeb-DF v2 dataset [52]. Top-right, top-left, bottom-right, bottom-left means false-positive, true-negative, true-positive, false-negative in order. bit lower than theirs, but we will undoubtedly have the advantage in terms of the number of parameters. Table 4 depicts the results obtained with the benchmarking of the Celeb-DF v2 dataset and DFDC dataset. Notable in the table is the F1-score of DFDC, we can see that the proposed model is robust in keeping fair assessment between fake videos and real videos and the F1-score was 0.9243, which was slightly higher than 0.919 of Heo et-al [4] proposal.