## 6 Experiment and Result Analysis
### Comparative Experimental Analysis  
#### 6.7.1 Comparison Experiment  
To assess the effectiveness of the proposed detection method, it was compared against existing state-of-the-art deepfake detection techniques. As mainstream evaluations usually emphasize AUC and ACC metrics, this study compares these metrics across the four subsets of the FaceForensics++ dataset with other methods. The proposed method has been thoroughly tested on this dataset. Table 11 presents the comparative results of AUC and ACC across various methods on the FaceForensics++ subsets (Deepfakes, Face2 Face, FaceSwap, NeuralTextures). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model Component**} & \multicolumn{8}{c}{**AUC (\%) and ACC (\%)**} \\ \hline SEAR-Net & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ EfficientNetB4 & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Gram Block & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ WSDAN & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline \multirow{4}{*}{AUC(\%)} & Deepfakes & 99.58 & 99.28 & 99.62 & 99.50 & 99.78 & 99.32 & 99.57 & 99.79 & 99.87 \\  & Face2 Face & 98.86 & 97.63 & 98.92 & 98.87 & 99.23 & 98.28 & 98.48 & 98.83 & 99.42 \\ FaceSwap & 97.34 & 98.09 & 99.34 & 99.24 & 99.03 & 97.68 & 98.25 & 99.14 & 99.29 \\  & NeuralTextures & 98.13 & 98.64 & 98.99 & 98.75 & 99.24 & 98.50 & 98.77 & 99.03 & 99.78 \\ \hline \multirow{4}{*}{ACC(\%)} & Deepfakes & 97.74 & 96.63 & 97.52 & 97.28 & 98.93 & 97.93 & 98.09 & 99.15 & 99.49 \\  & Face2 Face & 96.43 & 95.49 & 97.43 & 96.35 & 98.87 & 97.48 & 97.69 & 98.47 & 99.18 \\ \cline{1-1}  & FaceSwap & 95.48 & 96.39 & 97.73 & 97.24 & 98.85 & 97.21 & 97.47 & 98.94 & 99.11 \\ \cline{1-1}  & NeuralTextures & 96.99 & 97.53 & 97.84 & 97.43 & 98.82 & 97.35 & 97.25 & 98.91 & 99.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of the four different subsets of Faceforensics++ in the intra-frame. Table 11 displays the AUC and ACC performance metrics of the FG-TEFusionNet method proposed in this study, compared with several leading deepfake detection methods across the four subsets of FaceForensics++. From the table, it is demonstrated that the Long-Distance algorithm marginally outperforms our method on the Face2 Face dataset, with AUC and ACC values higher by 0.56% and 0.80%, respectively. However, on the Deepfakes, FaceSwap, and NeuralTextures datasets, our approach demonstrates improvements in AUC and ACC by 0.08%, 0.03%, and 1.17%, and 0.05%, 0.84%, and 5.78%, respectively. The most substantial enhancement was observed in the NeuralTextures dataset. This was likely due to the dataset's emphasis on texture-altering forgeries, where our method's inclusion of a texture feature module excels in capturing these nuanced features, thereby resulting in significant performance gains compared to other methods. In addition, the experimental results demonstrate substantial performance variations of the proposed multi-feature decision fusion framework (FG-TEFusionNet) across four deepfake categories, as detailed in Table 3. Notably, the model achieved superior detection capabilities for Deepfakes (AUC 99.87%/ACC 99.49%) and NeuralTextures (AUC 99.78%/ACC 99.03%), which were attributed to the synergistic operation of dual-stream architecture. Geometric landmarks were effectively captured through 3D facial mesh reconstruction to identify structural deviations caused by global face replacement in Deepfakes, while textural patterns were enhanced via the Gram texture feature module through gradient-based localization of synthetic artifacts.