## 4 Construction and Optimization for the Deepfake Detection Model
### Construction of the FG-TEFusionnet Model for Deepfake Detection
#### 4.3.2 Model Construction of Incorporating WSDAN and Image Texture Enhancement Module  
The improved SENet model relies solely on optimized geometric features of facial landmarks for discrimination, which effectively eliminates finer facial textures and retains only the external contour shapes. By comprehensively utilizing detailed facial texture features and incorporating a temporal information branch within video frames into the discriminative network, the accuracy and robustness of deepfake face video detection can be significantly improved. Consequently, this study introduces the Gram image texture enhancement module. Compared to baseline neural networks, this module excels in capturing facial texture features, acquiring larger texture sizes and more extensive texture patterns, thereby enhancing the model's capability to detect deepfake faces. Furthermore, the model integrates the WSDAN network, which further strengthens the extracted texture features, enhancing the model's robustness. In summary, the image texture enhancement module and the WSDAN network are integrated into EfficientNet, resulting in the design of TE-WSDAN-DDNet (Texture Enhancement and WSDAN with EfficientNet for Deepfake Detection) for deepfake face detection, as illustrated in Figure 11.  
As illustrated in Figure 11, the framework of TE-WSDAN-DDNet for deepfake face detection integrates the image texture enhancement module and the WSDAN network. The TE-WSDAN-DDNet model primarily comprises the Gram Block, WSDAN module, EfficientNet, and GAP Layer. Both shallow and deep layers of the network compute the Gram matrix to extract texture feature information at different levels. Specifically, texture enhancement blocks are added at five positions within EfficientNet: after the first convolution layer, after the second MBConv6, after the tenth MBConv6, after the twenty-second MBConv6, and before the final classification layer. Texture information is computed at each of these layers. This texture information from different network levels is concatenated and then combined with the backbone network of the EfficientNet network to produce image features containing multi-level texture information. The enhanced data derived from WSDAN is subsequently used to aid in deepfake detection. Finally, the features are input into the global average pooling module for the classification of the prediction results.  
The EfficientNet backbone network in this paper primarily relies on the MBConv module. This module performs convolutional operations on input feature maps to capture inter-pixel correlations within convolutional maps and subsequently extracts global features. The composition of the MBConv module is depicted in Figure 12. During the model's feature extraction process, Conv1 is initially employed to extract shallow-level information from the images using a \(3\times 3\) convolutional kernel and a stride of 2. Following this, batch normalization (BN) and Swish activation functions are incorporated to mitigate overfitting. In subsequent stages, seven MBConv modules are utilized for deep feature extraction. Among these, MBConv1 and MBConv6 employ expansion ratios of 1 and 6, respectively, with similar network structures. MBConv6, however, scales up the input channels by a factor of 6 to capture more intricate image features, necessitating five downsampling steps for extracting forged image texture features.  
Figure 11: Framework diagram of TE-WSDAN-DDNet incorporating image texture enhancement.  
Figure 12: Component structure of the MBConv module.