# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 4 Experiments
### Dataset  
To train and evaluate the proposed models, we utilized datasets such as Widerface [29], VGGFace2-HQ [30], FFHQ [17], and CelebA-HQ [31], with detailed information presented in Table 1. According to Table 1, it is noted that the images in these datasets often contain multiple faces and not just a single one, suggesting that reconstructed datasets using these sources could enable training and evaluation for scenarios with both single and multiple faces. Recognizing the significance of the face area's proportion in images for feature representation post-de-identification, we found that larger face regions could significantly alter the features. Table 2 shows the distribution of images based on the size of the face region across these datasets, indicating a bias in the proportion of faces within images. By reconstructing the training dataset to have a uniform distribution across various face-area ratios, we ensured consistent and effective model training regardless of the face region's ratio in the images. Advanced face-bounding-box detection was performed, using the yolov7-face [27] model, specifically the yolov7-w6+TTA variant, achieving 84 fps on a V100 GPU. The dataset now includes a minimum of 15,000 images for each face-area ratio, providing a robust basis for evaluation. For evaluating the performance before and after de-identification in image-copy-detection tasks, we employed the DISC21 [24] and CopyDays [32] datasets, and for video-copy detection the VCDB dataset was utilized. The composition of the datasets for image-copy detection is detailed in Table 3, which includes analyses of the total number of images and the quantity containing faces, showcasing the diversity and complexity of the datasets. The presence of faces in images plays a crucial role in image- and video-copy-detection tasks, particularly in assessing the efficiency of the proposed model's de-identification process. Quantifying the number of instances with visible faces allows for a better measurement of the de-identification impact applied by the model. This information is vital in evaluating how well the model distinguishes between the manipulated and original images under various conditions. Similarly, Table 4 presents the VCDB dataset composition, including the total number of video clips and the proportion containing faces. This metric is essential as it directly correlates with the model's ability to effectively obfuscate facial identities while preserving the integrity of non-target features, thus serving as a crucial indicator of model efficiency. The model's performance is measured not only in terms of its overall accuracy but also in terms of its precision in handling images and videos containing faces, ensuring that the data utility for copy-detection purposes is not compromised by de-identification processing. By including a large and diverse set of video clips in VCDB, the model's performance in consistently detecting and tracking de-identified faces within video streams can be comprehensively evaluated. Utilizing these datasets for evaluation allows for a thorough analysis of the model's performance across various scenarios, with a particular focus on the accuracy and reliability of face de-identification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Widerface [29]**} & \multicolumn{2}{c}{**VGGFace2**} & \multirow{2}{*}{**FFHQ [17]**} & \multirow{2}{*}{**CelebA-HQ [31]**} \\  & & & & \\ \hline Number of Images & 30,250 & 113,758 & 70,000 & 202,599 \\ Number of Identities & - & 903 & - & 10,177 \\ Number of Faces & 140,040 & 151,108 & 139,000 & 231,560 \\ Avg Number of Faces per Image & 4.63 & 1.33 & 1.99 & 1.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result of the proposed models in de-identification and feature-similarity experiments.