# Explainable AI for DeepFake Detection
## 4 Findings and Evaluation  
This research work employs three widely used CNN architectures for deepfake detection: Resnet50, Inception V3, and VGG-16. To assess how well these models perform, metrics such as accuracy, recall, precision, and F1-score are applied. The test data results are presented in Table 2.  
Figures 10-12 depict the learning curves for CNN models trained with various hyperparameters, illustrating the accuracy and loss trends over the course of the epochs. The plots reflect the results across 15 epochs. The Adam optimizer was consistently applied across all three models. ResNet-50 and Inception V3 were trained with a learning rate of 0.00001 and a batch size of 32, whereas VGG-16 was trained using a batch size of 16 and a learning rate of 0.0001. These hyperparameters were selected based on several experimental trials conducted on the models.  
This section of the proposed work aims to explain the underlying decision process of the deepfake detection models. After performing multiple experiments with a deepfake face dictionary obtained from the CelebAMask-HQ dataset, a network dissection report is generated for each model. The report provides information about the interpretable facial concepts by each model with different IoU scores. To test the explainability of the models, different threshold values are set to compare the features learned by different architectures. This provides an overview of how models learn different facial concepts based on segmentation. Figures 13-15 depict the number of interpretable units detected by each model with regard to the various facial regions for different threshold values. Figure 16 represent a sample of a few of the facial concepts, along with their IoU scores as detected by the models. In Tables 3-5, information about the amount of interpretable and uninterpretable units, along with the top IoU scores and their corresponding facial concepts, is listed. Additionally, the top intersection over union (IoU) scores and their associated facial concepts are included, reflecting the models' effectiveness in identifying critical facial features. Together, these results illustrate the comparative performance of the CNN architectures in capturing distinct and relevant features for distinguishing between real and fake images, underscoring their interpretability and relevance in deepfake detection tasks.  
Figure 14: Distribution of Interpretable Units per Facial Region with a Threshold value of 0.04.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **ResNet-50** & **VGG-16** & **InceptionV3** \\ \hline Interpretable units & 417/512 & 1875/2048 & 350/448 \\ Uninterpretable units & 95/512 & 173/2048 & 98/448 \\ Top IoU score & 0.4235 & 0.3524 & 0.4286 \\ Facial concept & hat & hat & cloth \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of Interpretations by CNN Models for a threshold value of 0.04.  
Figure 15: Distribution of Interpretable Units per Facial Region with a Threshold value of 0.005.  
Figure 16: Two examples for each model (**a**) Inception V3 (**b**) VGG-16 (**c**) ResNet-50.  
Figure 19: Dissection Histograms of ResNet-50.  
Figure 20: Dissection Histograms of Inception V3.  
Figure 18: Dissection Histograms of VGG-16.  
Figure 21: Number of Units Within Multiple Layers for VGG-16.  
Figure 22: Number of Units Within Multiple Layers for ResNet-50.