## 4 Experiment and Analysis
### Ablation Experiments
#### 4.3.2 Ablation Experiment on Corrupted Datasets  
The second ablation experiment adds artificial corruptions to standard images, such as Color_contrast, Gaussian_noise_color, and Jpeg_compression, generating three corrupted datasets. These corrupted datasets are Deepfakes-C, FaceSwap-C, and FaceShifer-C. They can simulate natural interference or artificial destruction in the natural environment. The robustness of Aug and MSE modules is verified on corrupted datasets. Table 4 shows the mode and severity of corruption used in experiments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Datasets** & **Acc/\%** & **AUC** \\ \hline \multirow{3}{*}{Baseline} & DeepFakes & 92.33 & 0.9794 \\  & FaceSwap & 88.57 & 0.9532 \\  & FaceShifer & 94.62 & 0.9869 \\ \hline \multirow{3}{*}{Baseline + Aug} & DeepFakes & 93.84 & 0.9837 \\  & FaceSwap & 94.41 & 0.9866 \\  & FaceShifer & 97.64 & 0.9954 \\ \hline \multirow{3}{*}{Baseline + MSE} & DeepFakes & 93.87 & 0.9855 \\  & FaceSwap & 92.91 & 0.9802 \\  & FaceShifer & 96.67 & 0.9922 \\ \hline \multirow{3}{*}{Baseline + Aug + MSE (This Work)} & DeepFakes & 96.49 & 0.9924 \\  & FaceSwap & 95.94 & 0.9897 \\ \cline{1-1}  & FaceShifer & 98.49 & 0.9974 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the gains generated by different modules under standard datasets based on ResNet50 network. The Metrics are Acc and AUC. The DeepFakes, FaceSwap, and FaceShifer are corrupted by different modes and severity of corruption. Some of the original images and their corrupted images in the three datasets are shown in Figure 7.  
We introduce the Aug and MSE module to enhance and train standard datasets. Then, we experiment with the pre-trained models on corrupted datasets, verifying the gain of Aug and MSE modules on model robustness. The Acc and AUC of all experiments' results are summed separately as mean values as the final indicators of experiments, as shown in Equations (22) and (23). The results of the second ablation experiment on corrupted datasets are shown in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Mode** & **Severity** \\ \hline None & — \\ Color\_saturation & 0.1; 0.2; 0.3; 0.4; 0.5 \\ Color\_contrast & 0.35; 0.475; 0.6; 0.725; 0.85 \\ Block\_wise & 16; 32; 48; 64; 80 \\ Gaussian\_noise\_color & 0.001; 0.002; 0.005; 0.01; 0.05 \\ Gaussian\_blur & 7; 9; 13; 17; 21 \\ J\_peg\_compression & 2; 3; 4; 5; 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mode and severity of corruptions used in ablation experiments. Figure 7: Some of the original and corrupted images: (**a**) The real images corrupted by the Gaussian\_noise\_color of severity 0.05 in FF++; (**b**) The images corrupted by the Color\_contrast of in DeepFakes of severity 0.8 in DeepFakes; (**c**) The images corrupted by the Block\_wise of severity 80 in FaceSwap; (**d**) The images corrupted by the J\_peg\_compression of severity 6 in FaceShifter. \[\text{Acc}_{\text{avg}} =\frac{\text{Acc}[0.35;0.475;0.6;0.725;0.85]^{\text{Color}\_{\text {saturast}}}+\cdots+\text{Acc}[6;5;4;3;2]\text{Peg}\_\text{compression}}{31} \tag{22}\] \[\text{AUC}_{\text{avg}} =\frac{\text{AUC}[0.35;0.475;0.6;0.725;0.85]^{\text{Color}\_{ \text{saturast}}}+\cdots+\text{AUC}[6;5;4;3;2]\text{Peg}\_\text{compression}}{31} \tag{23}\]  
The results show that the performance of ResNet50 on corrupted datasets is significantly lower than that on standard datasets, which indicates that ResNet50 has no practical detection ability for corrupted images. After introducing the Aug module in the preprocessing stage, the average Acc and AUC of detection models on three corrupted datasets have increased by 15.46% and 0.193, which proves that the pre-trained model on enhanced data has excellent robustness. After introducing the MSE module, the average Acc of the detection model has improved by 14.37%. The average AUC has improved by 0.178.