## 4 Experiment and Analysis
### Contrast Experiments  
To further verify the proposed algorithm's robustness improvement, a comparison experiment is designed to compare it with mainstream detection models. First, to effectively observe the variation in detection performance of mainstream detection models on standard and interference images, this experiment was set up to test these models on standard images. Four mainstream models, including MesoNet [19], XceptionNet [27], EfficientNet-B4 [28], and F3-Net [29], are selected for pre-training on DeepFakes, FaceSwap, and FaceShifer standard images. The experimental results of these mainstream algorithms on standard datasets are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Datasets** & **Acc/\%** & **AUC** \\ \hline \multirow{3}{*}{Baseline} & DeepFakes-C & 59.21 & 0.6121 \\  & FaceSwap-C & 60.04 & 0.6141 \\  & FaceShifer-C & 80.97 & 0.8873 \\ \hline \multirow{3}{*}{Baseline + Aug} & DeepFakes-C & 79.29 & 0.8752 \\  & FaceSwap-C & 77.85 & 0.8544 \\  & FaceShifer-C & 89.45 & 0.9617 \\ \hline \multirow{3}{*}{Baseline + MSE} & DeepFakes-C & 77.24 & 0.8254 \\  & FaceSwap-C & 81.36 & 0.8878 \\  & FaceShifer-C & 84.74 & 0.9329 \\ \hline \multirow{3}{*}{Baseline + Aug + MSE (This Work)} & DeepFakes-C & 90.63 & 0.9654 \\  & FaceSwap-C & 91.61 & 0.9703 \\ \cline{1-1}  & FaceShifer-C & 91.03 & 0.9719 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison the gains generated by different modules under corrupted datasets based on ResNet50 network. The Metrics are Acc and AUC. The experimental results in Table 6 show that current mainstream deepfake detection models can perform excellently on standard datasets. These models can classify forged images well. Then, these pre-trained models were used to perform robustness tests on the three generated interference datasets. Table 7 shows experimental results. As seen in Table 7, The results of these mainstream models on the corrupted test sets are much lower than those on standard datasets. These methods no longer have effective detection performance on post-interference datasets. However, the proposed method in this paper can still perform well. The experimental results illustrate that the robustness of the proposed method is much further ahead compared to mainstream models. To further demonstrate that the work in this paper outperforms mainstream detection models in terms of robustness, the sensitivity (TPR) and specificity (TNR) metrics are used to measure the detection performance of these models on positive and negative image samples after being corrupted. The results are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Datasets** & **Acc/\%** & **AUC** \\ \hline \multirow{3}{*}{XceptionNet} & DeepFakes & 95.65 & 0.9913 \\  & FaceSwap & 94.90 & 0.9897 \\  & FaceShifer & 97.36 & 0.9950 \\ \hline \multirow{3}{*}{MesoNet} & DeepFakes & 93.46 & 0.9891 \\  & FaceSwap & 87.33 & 0.9444 \\  & FaceShifer & 96.27 & 0.9939 \\ \hline \multirow{3}{*}{EfficientNet-B4} & DeepFakes & 95.00 & 0.9937 \\  & FaceSwap & 95.23 & 0.9892 \\  & FaceShifer & 98.03 & 0.9961 \\ \hline \multirow{3}{*}{F\({}^{3}\)-Net} & DeepFakes & 94.89 & 0.9912 \\  & FaceSwap & 95.15 & 0.9896 \\ \cline{1-1}  & FaceShifer & 98.01 & 0.9938 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of mainstream detection models on standard datasets with the metrics Acc and AUC.