## 6 Experiment and Result Analysis
### Analysis of Ablation Experiments  
To validate the role and effectiveness of the innovations introduced in this paper within the FG-TEFusionNet method, a quantitative analysis of the primary components of the model was conducted. To achieve this, ablation experiments were performed under consistent experimental conditions and settings to assess the impact of the fundamental components on the model's performance, thereby verifying the effectiveness of each component. These experiments aim to provide an in-depth evaluation of the performance contribution of each component. Accordingly, this study examined the effectiveness of each module by dividing the validation into inter-frame information feature extraction networks and intra-frame information feature extraction networks. In order to test the effectiveness of the SENet attention mechanism integrated into the SEAR-Net inter-frame information feature extraction network, tests were conducted under consistent experimental conditions. Table 9 summarizes the performance of the four different subsets of FaceForensics++ in the inter-frame information feature extraction branch. From the ablation experiment results shown in Table 9, it is evident that the introduction of the SENet attention component significantly improved the AUC and ACC metrics of the g1 network branch of the two-stream RNN across the four different subsets of FaceForensics++ (Deepfakes, Face2 Face, FaceSwap, NeuralTextures), with increases of 0.78%, 0.55%, 0.53%, and 2.15%, and 0.69%, 0.56%, 0.80%, and 0.59%, respectively. Furthermore, the AUC and ACC metrics of the inter-frame information feature extraction network, which includes both g1 and g2, also showed respective improvements of 0.68%, 0.41%, 0.21%, and 1.91%, and 0.88%, 0.94%, 0.25%, and 0.12%. This indicates that the SENet component effectively enhances the capability of extracting cross-frame information features from the input samples when geometric features are extracted, enabling the two-stream RNN network model to better learn temporal dimension forgery features. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Inter-Frame Information** & \multicolumn{4}{c}{**AUC(\%)**} & \multicolumn{4}{c}{**ACC(\%)**} \\ \hline \(g_{1}\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ \(g_{1}+g_{2}\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ SENet Attention & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ Deepfakes & 97.23 & 98.01 & 98.90 & 99.58 & 85.64 & 86.33 & 96.86 & 97.74 \\ Face2 Face & 97.69 & 98.24 & 98.45 & 98.86 & 85.43 & 85.99 & 95.49 & 96.43 \\ FaceSwap & 96.39 & 96.92 & 97.92 & 98.13 & 84.27 & 85.07 & 96.74 & 96.99 \\ NeuralTextures & 92.19 & 94.34 & 95.43 & 97.34 & 84.76 & 85.35 & 95.36 & 95.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the four different subsets of Faceforensics++ in the inter-frame. Additionally, SENet attention was also added to the g2 network branch of the RNN, but the results were not satisfactory. The g2 network branch represents the differences in the optimal facial feature points between adjacent frames, capturing geometric feature velocity information. This method is not effective for representing long-term temporal information and can even negatively impact accuracy. Therefore, SENet attention is primarily applied to the g1 network branch of the RNN in this study to achieve the best overall network performance. To validate the effectiveness of the Gram Block, WSDAN module, and EfficientNet within the TE-WSDAN-DDNet network model's intra-frame information feature extraction branch, ablation experiments were conducted.