## 3 Results
}}{y_{i}\text{ (TPR): True Positive Rate. }}\) \\ ROC AUC & \(\sum\limits_{i=1}^{n-1}\dfrac{(x_{i+1}-x_{i})\cdot(y_{i}+y_{i+1})}{2}\) & \(\dfrac{x_{i}\text{ (FPR): False Positive Rate. }}{y_{i}\text{ (TPR): True Positive Rate. }}\) \\  &  
The details presented in Table 2 provide an opportunity to evaluate the model's performance for each class separately. For the real class, the precision was determined as 0.69, the recall as 0.83, and the F1 score as 0.75. For the fake class, these values were measured as 0.96, 0.91, and 0.93, respectively. The overall accuracy of the model was calculated as 90%. This indicates that the model demonstrates strong performance, particularly in detecting fake videos, but requires further optimization for the classification of real videos. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline Real & 0.69 & 0.83 & 0.75 & 76 \\ \hline Fake & 0.96 & 0.91 & 0.93 & 320 \\ \hline Accuracy & & & 0.90 & 396 \\ \hline Macro-Avg. & 0.82 & 0.87 & 0.84 & 396 \\ \hline Weighted Avg. & 0.91 & 0.90 & 0.90 & 396 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification metrics showing the model’s class-based performance. Figure 6: Confusion matrix for test data. Figure 7: ROC curve and AUC of the model. The dashed blue line represents the performance of a random classifier, where predictions are no better than chance, with equal true and false positive rates. To summarize the overall performance of the model, weighted average values were used, as given in Table 3. This approach was chosen due to the imbalance in the test dataset, where the \"fake\" class contains more data. The model's precision was calculated as 0.91, the recall as 0.90, and the F1 score as 0.90, while the ROC AUC score was determined to be 0.94. These metrics indicate that the model demonstrates consistent performance across both classes and achieves successful classification at all threshold levels. The use of weighted average values minimizes the impact of dataset imbalance and provides a more realistic reflection of the model's overall performance. However, while these results confirm the model's superior performance in the \"fake\" class, they also highlight the need for improvement in the \"real\" class. Evaluating the data in this manner ensures a more reliable assessment of the model's classification success in real-world scenarios. To evaluate the generalizability of the model across datasets, a cross-dataset test was performed using a subset of the CelebDF-v2 dataset [34]. The evaluation included 10 real and 10 fake videos, selected to provide an initial assessment of the model's performance on unseen data. The results of this evaluation are summarized in Table 4, and the corresponding visualizations are provided in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline Real & 1.00 & 0.70 & 0.82 & 10 \\ \hline Fake & 0.96 & 0.91 & 0.93 & 10 \\ \hline Overall & 0.88 & 0.85 & 0.85 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification metrics showing the model’s class-based performance on the cross-dataset evaluation. Figure 8: (**a**) Confusion matrix showing classification performance on the cross-dataset evaluation; (**b**) ROC curve representing the model’s performance for the cross-dataset evaluation. The dashed blue line represents the performance of a random classifier, where predictions are no better than chance, with equal true and false positive rates. The classification performance metrics, including precision, recall, and F1 score, indicate that the model achieved an overall accuracy of 85% with a macro-average F1 score of 0.85. The ROC curve further highlights the model's classification capability, with an AUC score of 0.97, demonstrating robust performance on previously unseen data. The detailed metrics are presented in Table 4, reflecting the model's ability to distinguish between videos labeled REAL and FAKE effectively.