## 6 Experiment and Result Analysis
### Analysis of Feature Extraction Effectiveness
#### 6.3.2 Analysis of Texture Feature Extraction  
The following section introduces the effectiveness of the proposed image texture enhancement module in extracting texture features. A series of experiments were conducted on four distinct datasets from FaceForensic++, exploring training adjustments with incrementally increasing weight coefficients and evaluating training outcomes with different loss functions.  
To enhance the training effectiveness in extracting texture features, this study incrementally added weights to different layers of the EfficientNet model within the intra-frame network branch. This approach aimed to improve the model's detection performance across various texture features. This study employed a geometric progression with a set common ratio between adjacent terms (q = 1, 2, 3, 3.5, 4). Table 3 presents the AUC values tested on the EfficientNet network branch.  
Based on the results from Table 3, the best detection performance was achieved by setting the training incremental weight q to 3 when the texture feature module was added  
Figure 15: The Landmark method is employed to annotate and track facial landmarks in two sets of sample data. (The second column shows facial landmark annotation, and the third column depicts landmark tracking.).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & **Deepfakes** & **Face2 Face** & **FaceSwap** & **NeuralTextures** \\ \cline{2-5}  & **AUC** & **AUC** & **AUC** & **AUC** \\ \hline q = 1 & 98.75 & 97.93 & 98.71 & 97.43 \\ q = 2.5 & 98.99 & 98.54 & 99.02 & 98.49 \\ q = 3 & 99.50 & 98.87 & 99.24 & 98.75 \\ q = 3.5 & 99.08 & 98.24 & 98.57 & 98.05 \\ q = 4 & 98.33 & 96.74 & 96.36 & 95.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The AUC results from testing four different subsets of FaceForensics++ on the efficientnet network branch.  
to the EfficientNet network. As q increased, the detection AUC value gradually decreased, possibly due to a decline in the quality of texture features extracted by the network, thereby resulting in diminished detection effectiveness. Additionally, this study employed Earth Mover's Distance (EMD) as a loss function for model optimization. EMD is a measure used in content-based image retrieval to compute the distance between two distributions. Initially proposed by [PERSON] et al. [PERSON] et al. (2018) for visual problems, we formulate the following loss function based on the EMD algorithm, as shown in Equation (25).  
\[Texture_{Loss}=\sum_{l}w_{l}\sum_{i}(sorted(F_{i})-sorted(\hat{F}_{i}))^{2} \tag{25}\]  
In this study, the EMD loss function was integrated into the model. Building upon the incremental sequence with q = 3, experiments were conducted to train the L2 loss function for calculating the Gram matrix and the EMD loss function separately within EfficientNet. The detection AUC results are presented in Table 4.  
Table 4 shows that the Gram L2 loss function achieved superior AUC performance. The network model learns relationships between feature vectors of the original texture images, yielding richer and more detailed texture features. Conversely, modifying the EMD loss results in poorer texture capture due to its sorted principle. This underscores the image texture enhancement module's role in aiding the model to capture forgery features across local and global perspectives. Integrated with the WSDAN network, it enables the backbone network to prioritize areas with prominent textures, thereby improving detection accuracy.