# Synthetic Face Discrimination via Learned Image Compression
## 4 Methodology and Experimental Results
### Results
#### 4.3.2 Robustness  
We experimented with post-processing operations to see how well the proposed method fares against image manipulation and examine its robustness. The results are reported in Tables 5 and 6. There are also visual representations of the exported data in Figures 9 and 10. The results clearly show that the proposed method is more effective for StyleGAN compared to StyleGAN2, but this does not hold true for the processed images. After post-processing, the second dataset seems to fare better on the deepfake detection front. This is interesting, given the fact that StyleGAN2 is more recent, and thus the generated face images are more realistic. We observe that our model is less impacted by Gaussian \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Precision/Recall**} \\ \hline  & **Our Method** & **ResNet50** \\ \hline StyleGAN & 98.60/99.20 & **100.0/99.40** \\ StyleGAN2 & 95.20/95.40 & **100.0/99.40** \\ Stable Diffusion (our model trained on StyleGAN) & **99.20/99.50** & 49.84/99.40 \\ Stable Diffusion (our model trained on StyleGAN2) & **98.81/99.40** & 49.84/**99.40** \\ \hline \hline \end{tabular} Bold entries indicate the best results for each scenario.  
\end{table} 
Table 4: Precision and recall results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**ACC/AUC**} \\ \hline  & **Our Method** & **ResNet50** \\ \hline StyleGAN & 98.90/**100.0** & **99.70/100.0** \\ StyleGAN2 & 95.30/99.0 & **99.70/100.0** \\ Stable Diffusion (our model trained on StyleGAN) & **99.00/100.0** & 49.70/32.50 \\ Stable Diffusion (our model trained on StyleGAN2) & **99.10/99.90** & 49.70/32.50 \\ \hline \hline \end{tabular} Bold entries indicate the best results for each scenario.  
\end{table} 
Table 3: Accuracy (ACC) and AUC results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**ACC/AUC**} \\ \hline  & **Our Method** & **ResNet50** \\ \hline StyleGAN & 98.90/**100.0** & **99.70/100.0** \\ StyleGAN2 & 95.30/99.0 & **99.70/100.0** \\ Stable Diffusion (our model trained on StyleGAN) & **99.00/100.0** & 49.70/32.50 \\ Stable Diffusion (our model trained on StyleGAN2) & **99.10/99.90** & 49.70/32.50 \\ \hline \hline \end{tabular} Bold entries indicate the best results for each scenario.  
\end{table} 
Table 4: Precision and recall results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Processing** & \multicolumn{6}{c}{**ACC/AUC**} \\
**Operation** & \multicolumn{6}{c}{**StyleGAN2**} & \multicolumn{6}{c}{**Style Diffusion (Our Model Trained on StyleGAN)**} & \multicolumn{6}{c}{**Stable Diffusion (Our Model Trained on StyleGAN2)**} \\ \hline  & **Our Method** & **ResNet50** & **Our Method** & **ResNet50** & **Our Method** & **ResNet50** & **Our Method** & **ResNet50** \\ \hline Gaussian noise (\(\sigma^{2}\) = 0.01) & **97.90/99.30** & 64.60/58.30 & **95.30/98.90** & 64.60/64.80 & **98.30/99.50** & 49.90/54.20 & **97.70/99.80** & 49.90/54.20 \\ \hline \hline \begin{tabular}{c} Median filter (3 \(\times\) 3) \\ \end{tabular} & 88.20/97.30 & **99.90/99.90** & 93.50/97.90 & **99.80/99.90** & **95.30/99.10** & 49.90/51.10 & **96.40/98.90** & 49.90/51.10 \\ \hline \
\begin{tabular}{c} JPEG (QF = 90) \\ \end{tabular} & 94.00/97.10 & **99.70/99.80** & 95.30/98.80 & **99.70/99.80** & **98.60/99.90** & 49.70/42.60 & **98.30/99.80** & 49.70/42.60 \\ \hline
\begin{tabular}{c} Cropping (512 \(\times\) 512) \\ \end{tabular} & 98.90/**100.0** & **99.30/98.70** & 95.30/**99.0** & **99.20/98.70** & **99.00/100.0** & 49.30/39.50 & **99.10/99.90** & 49.30/39.50 \\ \hline Resize (0.5) & 98.40/99.60 & **100.0/99.90** & **94.40/98.80** & 88.60/91.30 & **98.50/99.80** & 50.00/50.80 & **99.20/99.90** & 50.00/50.80 \\ \hline Resize (1.5) & 94.10/98.20 & **100.0/100.0** & 92.30/98.10 & **100.0/100.0** & **94.70/98.60** & 50.00/50.40 & **93.90/98.90** & 50.00/50.40 \\ \hline \hline \end{tabular} Bold entries indicate the best results for each scenario.  
\end{table}