## 3 Results  
To evaluate the performance of the model during the training process and measure its ability to distinguish between fake (FAKE) and real (REAL) videos, various metrics were calculated. This process provides a broad evaluation spectrum, ranging from basic measures such as accuracy, precision, recall, and F1 and F2 scores to more complex performance analyses like the Matthews Correlation Coefficient (MCC) and ROC AUC. At the end of training, the overall success of the model was analyzed using tools such as classification  
Figure 5: Data processing flowchart for the model training process. reports, confusion matrices, and ROC curves. These analyses play a critical role in assessing the model's potential for real-world applications in deepfake detection. The classification metrics used to evaluate the model's performance are summarized in Table 1. These metrics--precision, recall, F1 score, accuracy, and ROC AUC--offer a comprehensive framework for assessing the model's ability to classify fake and real videos. Precision evaluates the proportion of correctly identified positive predictions among all positive predictions, providing insight into the model's accuracy in predicting the positive class [30]. Recall, also known as sensitivity, measures the proportion of actual positive instances correctly classified [30]. The F1 score balances precision and recall by considering both false positives and false negatives, offering a single metric that reflects the model's overall effectiveness in positive classification tasks [31]. Accuracy calculates the proportion of correctly classified instances, including both positive and negative examples, across the entire dataset [32]. Finally, the ROC AUC quantifies the model's ability to differentiate between classes by calculating the area under the Receiver Operating Characteristic curve, which is constructed based on False Positive Rate (FPR) and True Positive Rate (TPR) values across various thresholds [33]. The descriptions in the table provide detailed explanations of the parameters used in these calculations, ensuring clarity and interpretability for readers. To visualize the model's performance, the confusion matrix shown in Figure 6 categorizes the model's correct and incorrect classifications into four main categories. For real videos, the number of correct classifications was 63, while the number of incorrect classifications was 13. Similarly, for fake videos, the number of correct classifications was 292, and the number of incorrect classifications was 28. The confusion matrix reveals that the model exhibits a very high success rate in distinguishing fake videos. However, its relatively lower performance in real videos indicates the need for optimization in this data type. The ROC curve in Figure 7 was used to evaluate the classification performance of the model. The area under the ROC curve (AUC) was calculated as 0.94, indicating that the model possesses a high discriminatory power. The curve illustrates the relationship between the False Positive Rate (FPR) and the True Positive Rate (TPR) across all threshold values. These results emphasize the model's strong performance, particularly in accurately classifying fake content. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Formula** & **Description** \\ \hline Precision & TP/(TP + FP) & TP (True Positive): correctly predicted positives \\  & TP/(TP + FN) & TP (True Positive): correctly predicted positive \\  & TP/(TP + FN) & instances. FN (False Negative): positive instances \\  & TP/(TP + FN) & incorrectly predicted as negative. \\ \hline F1 Score & \(2\times\dfrac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}}\) & Combines precision and recall into a single metric. \\  & TP/(TP + FN) & Balances the trade-off between false positives (FPs) \\  & TP/(TP + FN) & TN (True Negative): correctly predicted negatives. \\ Accuracy & \(\dfrac{\text{TP}+\text{TN}}{\text{Total Number of Instances}}\) & \(\dfrac{x_{i}\text{ (FPR): False Positive Rate.