# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 4 Experimental Setup and Analysis of Results
### Robustness Analysis
#### 4.3.3 Social Network Sharing  
The identification of synthetic media over social networks is a well-known challenge in media forensics [32], and an open issue for GAN-generated image detection [33]. Social media typically apply custom data compression algorithms to reduce the size and the quality of the images to be stored on data centers or costumer's devices, thus hindering post hoc analyses.  
We then test the capabilities of the developed classifiers to generalize to the image data shared on social networks. It is worth noticing that in this case, the models are exactly the ones considered in the classifiers developed in Section 4.2; thus, they are trained entirely on images with no sharing operations. Instead, the testing set is a subset of the recently published [[https://zenodo.org/record/7065064#.Y2](https://zenodo.org/record/7065064#.Y2) to3 ZrzMKdZ]([https://zenodo.org/record/7065064#.Y2](https://zenodo.org/record/7065064#.Y2) to3 ZrzMKdZ) (accessed on 30  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Train/Test** & **NO COMP** & **100** & **95** & **90** & **80** & **70** \\ \hline NO COMP & 87.50 & 84.37 & 90.00 & 85.62 & 89.37 & 90.00 \\
100 & 85.62 & 88.12 & 86.87 & 86.25 & 88.12 & 88.12 \\
95 & 85.00 & 85.00 & 86.87 & 85.62 & 87.50 & 86.25 \\
90 & 83.75 & 85.62 & 85.62 & 82.50 & 86.87 & 86.25 \\
80 & 87.50 & 90.00 & 88.75 & 85.62 & 89.37 & 90.00 \\
70 & 85.00 & 89.37 & 86.25 & 89.37 & 90.00 & 90.62 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy obtained by \(LM_{68}\) with different JPEG quality factors (reported in percentage).  
Figure 14: Examples of face images before (top row) and after (bottom row) the reconstruction when different resizing factors are applied as post-processing.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Train/Test** & **NO COMP** & **100** & **95** & **90** & **80** & **70** \\ \hline NO COMP & 88.75 & 88.12 & 88.75 & 85.00 & 90.62 & 88.75 \\
100 &  
December 2022) dataset, which is composed of StyleGAN2 images and real images (extracted from FFHQ) before and after the upload and download from three different social networks: Facebook, Telegram, and Twitter. We randomly select 100 synthetic and 100 real images and extract their shared versions through the three platforms.  
Examples of shared images and their reconstructions are reported in Figure 16.  
After inversion, we obtain a reconstruction for each of them. If needed, a rescaling operation is applied to fit the input size of the inversion algorithm. We then extract the feature representations and differential vectors, and test them through the corresponding classifiers already trained in Section 4.2 for the _FFHQ_ vs. _SG2_ scenario.  
The results are reported in Table 12, where the accuracies of each binary classification scenario (one for each platform) are reported column-wise. As highlighted in [33], all platforms apply JPEG compression (quality factor between 80 and 90), and Facebook also resizes the images by a 0.7 factor.  
Interestingly, the landmark-based features yield remarkable performance in all cases, thus demonstrating a high robustness against this realistic kind of post-processing. In particular, they achieve a maximum accuracy in the Facebook case, which is the more critical one for FaceNet-based features, and also for the general purpose deep networks analyzed in [33].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Facebook** & **Telegram** & **Twitter** & **All** \\ \hline _FN\({}_{128}\)_ & 73 & 85 & 77 & 80 \\ _FN\({}_{512}\)_ & 74 & 82 & 78 & 76 \\ _LM\({}_{68}\)_ & 96 & 97 & 98 & 96 \\ _LM\({}_{136}\)_ & 100 & 98 & 96 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Accuracy of the different classifiers (SVM model) on a subset of the TrueFace dataset, including images shared through different social media platforms (reported in percentage).  
Figure 15: Examples of face images before (top row) and after (bottom row) the reconstruction when different JPEG quality factors are applied as post-processing.