# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 4 Experiments
### Face Verification and Feature Similarity  
Table 5 in this study categorizes the performance of the proposed models based on the face-region ratio within images. The Structural Similarity Index (SSIM) indicates that values closer to 1 signify higher similarity between the face regions before and after de-identification. The Peak Signal-to-Noise Ratio (PSNR) suggests that values closer to 0 mean higher similarity between the face regions before and after de-identification. Additionally, cosine similarity, with values near 1, denotes a high similarity between two features, demonstrating the model's efficiency in generating privacy-preserving images, even when face regions vary and occupy a significant portion of the image. Table 6 shows the de-identified images from each model, categorized by the size ratio of face regions within each \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Detail** & **Value** \\ \hline \multirow{2}{*}{Number of Videos} & Core Videos & 528 \\  & Background Videos & 100,000 \\ \hline \multirow{2}{*}{Number of Videos} & Core Videos & 528 \\  & Background Videos & 97,429 \\ \hline \multirow{2}{*}{Length} & Max Length of Video & 2662 s \\  & Min Length of Video & 3 s \\  & Avg Length of Video & 183.63 s \\ \hline \multirow{2}{*}{Resolution} & Range & 320 \(\times\) 214-1920 \(\times\) 1080 \\  & Number of Varieties & 43 distinct \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of the VCDB dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Detail**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}  & & **DISC21 [24]** & **CopyDays [32]** \\ \hline \multirow{3}{*}{Number of Images} & Dev Query & 50,000 & 1000 \\  & Test Query & 50,000 & - \\ \cline{1-1}  & Reference & 1,000,000 & 3212 \\ \cline{1-1}  & Train & 1,000,000 & 1000 \\ \hline \multirow{3}{*}{Number of Images} & Dev Query & 3369 & 58 \\ \cline{1-1}  & Test Query & 2925 & - \\ \cline{1-1}  & Reference & 86,539 & 332 \\ \cline{1-1}  & Train & 80,645 & 65 \\ \hline \multirow{2}{*}{Resolution} & Range & 32 \(\times\) 32-1024 \(\times\) 1024 & 56 \(\times\) 72-3008 \(\times\) 2000 \\ \cline{1-1}  & \# of Varieties & 10,860 distinct & 1071 distinct \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the image-copy-detection datasets. identified query images and videos alongside the non-de-identified reference images and videos, to assess the before-and-after effects of de-identification. In this context, the evaluation of the image-copy-detection task specifically focused on applying the de-identification processes to query images and videos. In the case of image-copy detection, the performance labeled 'Total' in Table 7 was evaluated using the entire DISC21 dataset, which corresponds to the 'Dev Query' and 'Reference' categories in Table 3's '# of Images', while 'Only Face' was evaluated using only the 'Dev Query' and 'Reference' images from the '# of Images Containing Faces' category. Similarly, for video-copy detection, the 'Total' performance in Table 8 was assessed using all the videos in the VCDB dataset, corresponding to the 'Core Videos' in Table 4's '# of Videos', and 'Only Face' was assessed using only the 'Core Videos' from the '# of Videos Containing Faces' category. We performed a comparative analysis of the performance before and after the application of de-identification, using the evaluation methodologies described in [24; 26]. Presented in Tables 7 and 8, the results indicate no significant alteration in performance for both the 'Total' and 'Only Face' categories following de-identification, which demonstrates our model's effectiveness in preserving privacy without compromising its ability to detect copies. This underscores the utility of our approach in protecting personal information, as it maintains robust copy-detection capabilities in various conditions, whether faces are present or not in the media.