# Synthetic Face Discrimination via Learned Image Compression
## 4 Methodology and Experimental Results
### Results
#### 4.3.2 Robustness

Table 5: Accuracy (ACC) and AUC results for “attacked” images under various image processing operations. noise compared to ResNet50. The cropping also has no effect whatsoever, which was to be expected since we used a cropped version of the image anyway. The median filter affects our model more than ResNet50, with a 10% decline in StyleGAN accuracy. On the other hand, on StyleGAN2 it seems to work much better, with a less than 2% loss of accuracy. The proposed method also responds well to JPEG compression. However, the present work surpasses the performance of ResNet50 for the stable-diffusion-generated dataset for all types of \"attacks\". Our technique maintains a stable performance of high accuracy, in most cases more than 90%. ResNet50, on the other hand, once again only detects real face images, failing in recognizing the diffusion-generated ones. In Figure 11, the performance of different JPEG quality factors is given. It is interesting that while for StyleGAN2 our method has a consistent performance, for StyleGAN it seems to improve as the quality factor increases. As was stated above, the proposed method performs significantly better than ResNet50 when Gaussian noise is added to the images. This is caused by the nature of the \"attack\". Gaussian noise affects the DWT of an image in several ways, primarily through the introduction of high-frequency components. It typically manifests as random variations in pixel values, predominantly affecting the high-frequency components of an image. During the DWT process, these high-frequency components are mapped to the detail coefficients. Consequently, the presence of Gaussian noise increases the magnitude of these detail coefficients. The diagonal detail that contains the high-frequency coefficients is extremely important to our method, as it displays the biggest variance between real and synthetic images. As a result, our compression-based technique performs much better than ResNet50 when faced with Gaussian noise. In order to prove this point even further, Figure 12 shows the behavior of both techniques when the images are \"attacked\" with Gaussian noise of different variances \(\sigma^{2}\), with the mean value being consistently zero. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Processing**} & \multicolumn{6}{c}{**Precision/Recall**} \\
**Operation** & & & & & **Stable Diffusion (Our** & **Stable Diffusion (Our** & **Model Trained on** & **Model Trained on** & **StyleGAN2** \\  & & & & & **StyleGAN)** & **ResNet50** \\  & **Our** & & & & **Our** & **Method** & **ResNet50** & **Our** & **Method** & **ResNet50** \\  & **Method** & & & & & & & \\ \hline Gaussian & & & & & & & & \\ noise (\(\sigma^{2}\) = 0.01) & & & & & & & & \\ \hline Median & & & & & & & & \\ filter (3 \(\times\) 3) & & & & & & & & \\ JPEG (QF = 90) & 92.64/95.60 & **100.0/99.40** & 94.50/96.20 & **100.0/99.40** & **98.80**/98.40 & 49.84/**99.40** & **98.20**/98.40 & 49.84/**99.40** \\ \hline Cropping (512 \(\times\) 512) & 98.60/**99.20** & **100.0/98.60** & 95.20/95.40 & **100.0/98.60** & **99.20/99.50** & 49.65/98.60 & **98.81**/**99.40** & 49.30/98.60 \\ \hline Resize (0.5) & 98.99/97.80 & **100.0/100.0** & **92.05**/97.20 & 81.43/**100.0** & **98.50**/99.80 & 50.00/**100.00** & **99.20**/99.90 & 50.00/**100.00** \\ \hline Resize (1.5) & 93.00/97.40 & **100.0/100.0** & 92.50/93.20 & **100.0/100.0** & **94.60**/94.80 & 50.00/**100.00** & **93.10**/93.60 & 50.00/**100.00** \\ \hline \hline \end{tabular} Bold entries indicate the best results for each scenario.  
\end{table} 
Table 6: Precision and recall results for “attacked” images under various image processing operations. Figure 10: Accuracy, AUC, precision, and recall results comparison for “attacked” images resized by a factor of 0.5. Figure 9: Accuracy, AUC, precision, and recall results comparison.