## 4 Experimental Results
### Visualization of Model Decisions  
To analyze how the evaluated models make decisions and localize features indicative of deepfake manipulation, Grad-CAM (Gradient-weighted Class Activation Mapping) was employed. Rather than relying solely on visual overlays, we quantified key aspects of the models' Grad-CAM activations to provide a more detailed understanding of their behavior. This quantitative approach enables a deeper comparison of the models' strengths and limitations in detecting manipulation artifacts. Quantitative Metrics: Four key metrics were derived from the Grad-CAM activations to evaluate the models:  
* Attention Coverage (%): The percentage of the Grad-CAM heatmap focused on manipulation-prone regions, such as the jawline, lips, and eyes. Higher coverage indicates better localization of relevant features. * Average Activation Intensity: The mean intensity of Grad-CAM activations in manipulation-prone regions, reflecting the model's confidence in its focus areas. * False Region Attention (FRA, %): The proportion of Grad-CAM activation present in irrelevant regions (e.g., background or non-facial areas). Lower FRA indicates better focus on manipulation-specific regions. * Manipulation Detection Rate (MDR, %): The percentage of fake frames where the model's Grad-CAM activations correctly highlight manipulation-prone regions. Results: Table 8 summarizes the results for the three evaluated models: XCeption, ResNet-50, and VGG16. The metrics highlight significant differences in how these models localize and interpret manipulation artifacts. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Clean Acc.** & **Adv. Acc.** & **Noise Acc.** & **Video-Level Acc.** & **Calib. (ECE)** & **GPU Mem** & **Err. Red.** \\  & **(DFDC, \%)** & **(\%)** & **(\%)** & **(DFDC, \%)** & **(\%)** & **(MB)** & **(Human, \%)** \\ \hline XCeption & 89.2\% & 79.1\% & 84.8\% & 90.7\% & 4.6\% & 1400 & 30 \\ ResNet-50 & 72.8\% & 64.2\% & 68.0\% & 73.9\% & 9.3\% & 1100 & 49 \\ VGG16 & 85.5\% & 74.3\% & 81.9\% & 86.9\% & 4.1\% & 1800 & 32 \\ MobileNet V2 & 80.2\% & 69.4\% & 76.5\% & 81.1\% & 5.2\% & 800 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Consolidated view of key metrics and observations. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model Comparison** & \(p\)**-Value** & **Significant** \\ \hline XCeption vs. ResNet-50 & \(<\)0.001 & Yes \\ XCeption vs. VGG16 & \(<\)0.01 & Yes \\ VGG16 vs. ResNet-50 & \(<\)0.001 & Yes \\ XCeption vs. MobileNet V2 & \(<\)0.001 & Yes \\ VGG16 vs. MobileNet V2 & \(<\)0.001 & Yes \\ ResNet-50 vs. MobileNet V2 & \(<\)0.001 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistically significant test results (paired \(t\)-test on accuracy). Analysis of the Models' Decisions: The metrics in Table 8 reveal important differences in the behavior of the models:  
* Xception: With the highest attention coverage (82.3%) and the lowest FRA (5.4%), Xception demonstrates superior focus on manipulation-prone regions. Its high activation intensity (0.78) and MDR (91.6%) indicate confidence and precision in detecting deepfake artifacts. These results align with its overall high accuracy and generalization capabilities. * ResNet-50: ResNet-50 exhibits lower attention coverage (65.2%) and the highest FRA (15.3%), suggesting that its attention is often distributed across irrelevant areas, such as the background. Its weaker localization and MDR (75.8%) highlight the need for architectural improvements or better training strategies to refine focus. * VGG16: VGG16 strikes a balance between attention coverage (78.7%) and FRA (7.9%). Its relatively high activation intensity (0.74) and MDR (88.1%) demonstrate strong performance in identifying subtle manipulation artifacts, making it particularly effective in forensic applications. Implications for Model Optimization: These findings provide valuable insights into the models' decision-making processes:  
* Xception: Its precise focus and low FRA make it suitable for real-time applications.