## 6 Experiment and Result Analysis
### Comparative Experimental Analysis
#### 6.7.1 Comparison Experiment
In contrast, localized manipulations in Face2 Face (AUC 99.42%) and FaceSwap (AUC 99.29%) exhibited micro-movement patterns that closely resembled authentic expressions, resulting in constrained feature discriminability within the dual-stream framework. This limitation precipitated a 0.38% accuracy decline compared to Deepfakes detection performance. Table 11 also highlights that, apart from the Long-Distance method, FG-TEFusionNet consistently achieves superior AUC and ACC metrics compared to all other algorithms. Specifically, in terms of AUC metrics, this study outperformed the state-of-the-art Face X-ray and LipForensics by 0.35%, 4.88%, 6.06%, and 7.21%, and 0.33%, 0.07%, 9.26%, and 0.64% on the Deepfakes, Face2 Face, FaceSwap, and NeuralTextures datasets, respectively. The results in the table demonstrate that our method maintained stable and robust detection performance across these four datasets, indicating minimal variance in detection accuracy. In contrast, LipForensics exhibited unstable detection performance, especially with a \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Deepfakes**} & \multicolumn{2}{c}{**Face2 Face**} & \multicolumn{2}{c}{**FaceSwap**} & \multicolumn{2}{c}{**NeuralTextures**} & \multirow{2}{*}{**Date**} \\ \cline{2-2} \cline{5-8}  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & \\ \hline MesoNet [20] & \(-\) & 87.27 & \(-\) & 56.23 & \(-\) & 61.17 & \(-\) & 40.67 & 2018 \\ Capsule [43] & 96.01 & 95.68 & 94.15 & 92.91 & 91.89 & 92.85 & 89.55 & 87.35 & 2019 \\ CNN+GRU [7] & 97.62 & 86.26 & 85.8 & 82.04 & 86.64 & 77.31 & 79.62 & 76.45 & 2019 \\ XceptionNet [13] & 98.68 & 95.35 & 93.77 & 83.48 & 98.82 & 98.84 & 97.18 & 95.84 & 2019 \\ EfficientNetB4 [18] & 99.28 & 96.63 & 97.63 & 95.49 & 99.34 & 96.38 & 98.99 & 97.53 & 2019 \\ F3-Net [44] & \(-\) & 98.62 & \(-\) & 95.84 & \(-\) & 97.43 & \(-\) & 86.03 & 2020 \\ Face X-ray [21] & 99.52 & \(-\) & 94.54 & \(-\) & 93.23 & \(-\) & 92.57 & \(-\) & 2020 \\ SPSL [22] & 98.50 & 93.48 & 94.62 & 86.02 & 98.10 & 92.26 & 84.23 & 76.78 & 2021 \\ LipForensics -Scratch [45] & 93.05 & \(-\) & 98.76 & \(-\) & 58.76 & \(-\) & 98.34 & \(-\) & 2021 \\ LipForensics [45] & 99.54 & \(-\) & 99.35 & \(-\) & 90.03 & \(-\) & 99.14 & \(-\) & 2021 \\ LRNet [32] & 98.90 & 95.83 & 98.45 & 94.79 & 97.92 & 93.87 & 95.43 & 92.54 & 2021 \\ M2 TR [23] & 98.65 & 94.23 & 97.64 & 93.54 & 96.74 & 94.97 & 95.47 & 93.39 & 2022 \\ Long-Distance [24] & 99.79 & 99.44 & **99.98** & **99.98** & 99.26 & 98.27 & 98.61 & 93.25 & 2023 \\
**FG-TEFusionNet (Ours)** & **99.87** & **99.49** & 99.42 & 99.18 & **99.29** & **99.11** & **99.78** & **99.03** & **2024** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of AUC and ACC results across different methods on the four subsets of the FaceForensics++ dataset. significant performance decline observed on the FaceSwap dataset. Moreover, LipForensics heavily relies on fixed feature extractors pre-trained specifically for predefined tasks within the dataset, leading to substantial performance degradation when trained from scratch. In terms of ACC metrics, improvements over the F3-Net method were observed across the four datasets: 0.87%, 3.34%, 1.68%, and 13%, respectively. Certain methods leverage frequency-domain feature extraction to enhance classification. For instance, M2 TR enhances detection performance by integrating frequency-domain features into spatial information-based models. Moreover, the F3-Net method demonstrates effective performance on low-quality data using frequency-domain techniques. In comparison to the CNN-GRU method, which generally shows poorer overall detection efficacy, our approach notably enhances performance on the Face2 Face, FaceSwap, and NeuralTextures forgery datasets, with most gains on Deepfakes. CNN-GRU excels on Deepfakes due to its robust handling of long-term dependencies, though it may overfit and underperform on shorter sequences.