# Explainable AI for DeepFake Detection
## 5 Discussion  
This work conducted various studies to understand the internal parts of the developed CNN models by interpreting the facial features learned by these models. It is known that neural networks can learn features through their hidden layers, and they are considered as black box models. In Section 4 (Findings and Evaluation), the outcomes of the explainable AI analysis are presented, which reveal the internal neuronal representation of the three CNN models used for deepfake detection. The data in Section 4 form the basis for the detailed insights discussed here. Specifically, the network dissection results, as summarized in Tables 3-5, provide evidence that the units of the CNN models have learned various facial concepts. These results show a balance between interpretable and uninterpretable units, depending on the threshold value used. However, there are fewer uninterpretable units for a threshold of 0.4, and the types of different facial concepts learned by the models are comparatively fewer. All three models mostly detected the cheek region as the major concept, followed by the hair region. With the decreasing threshold value, e.g., to 0.005, the number of uninterpretable units increased, which leads to fewer units detecting concepts. In comparison to all three threshold values, 0.04 is the best threshold value as it identifies a variety of facial concepts for all CNN architectures used in the proposed study. It is observed from Figures 13-15 that the hair region is one of the top facial regions detected by all three models for all threshold values. The cheek region contributes the most for the threshold value of 0.4 for all three models, whereas for threshold values of 0.04 and 0.005, the neck and eye regions are detected by VGG-16 and ResNet-50, respectively. On the other hand, Inception V3 detects eye and ear regions for threshold values 0.04 and 0.005 respectively. From Tables 3-5, it is noticed that the top facial concepts learned by models include skin, hat, cloth, and earrings. In spite of the fact that higher threshold values lead to better IoU scores, the types of facial features learned by the models are reduced in number. Hence, the representation of individual facial concepts learned by models is shown for a threshold value of 0.04, as given in Figures 20-22, which covers all the concepts. All the experiments for the above results are carried out for dissecting the final layers of the neural networks. This is because dissecting the earlier layers of the models should be avoided due to the model's inability to learn higher-level concepts. This statement can be proven by analyzing the results from Figure 23. From the figure, it is observed that with the initial layers, the proportion of the number of interpretable units is smaller compared to the final layers of the models. As previously stated, applying the IoU approach based on the network dissection algorithm to the images from our deepfake face dictionary makes it difficult to conclude that a concept with an IoU score similar to the top concept is interpretable without conducting a thorough analysis of the activations associated with that concept in relation to other concepts present in the same facial regions. Consequently, we discover that a unit can correspond to several concepts by constructing a hierarchy among the concepts from that specific facial region. This characteristic is explained in Figure 19 and clearly shows how challenging it is for a neuron to differentiate between concepts that appear similar but possess different features.