# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 4 Experiments
### Face Verification and Feature Similarity
diverse range of face-region sizes and an even distribution of images across different face-region ratios, emphasizing the importance of meticulous data preprocessing and selection in future research. The two de-identification methods proposed--namely, the feature inversion and D2 GAN methods--also presented some limitations. The feature-inversion method, while generating images with emphasized contours, was found to allow for recognition upon close inspection. This underscores the importance of carefully balancing the need for personal-data protection and data utility in the de-identification process. By contrast, the D2 GAN method effectively provided de-identification by generating blurred facial images, making it difficult to recognize faces. However, this strong de-identification could reduce the utility of the image, necessitating a careful balance in situations where content utility is a priority. For the feature-inversion model, if we focus more on face-image-similarity loss by adjusting the loss function, the face image can appear more transparent while effectively protecting personally identifiable information. Conversely, if we focus more on feature-similarity loss, the image will be trained to be less identifiable but more similar in important features. This suggests a methodology that satisfies the need for privacy while maintaining the usefulness of the data. On the other hand, by adjusting the amount of facial blur, the D2 GAN model can be trained with a face shape for strong de-identification, as shown in our experimental results, or it can be trained with a sharper face shape. This tunability allows for setting the privacy level flexibly according to the requirements of a specific application. Personal-data protection is a core issue in the digital era, with facial data being among the most sensitive and crucial identifiers. There is a potential conflict between the proliferation of illegal content and the protection of personal information. The proposed models--namely, the feature inversion and D2 GAN methods--can play a significant role in resolving this conflict. The feature-inversion method generates images based on facial contours, useful in situations where maintaining the natural appearance of the image is essential. However, caution is required in scenarios demanding high levels of personal-data protection, due to the potential risk of re-identification. On the other hand, the D2 GAN method enhances personal-data protection by effectively concealing facial information through blurring. While this strong de-identification is beneficial in highly sensitive situations, it is important to find the right balance in contexts where the utility of the content is valued. Considering these factors, finding a balance between personal-data protection and data utility is crucial. Additionally, in-depth research into how these de-identification methods respond to potential re-identification attacks or vulnerabilities is necessary. The feature-inversion method, due to its emphasis on contours, might be vulnerable to deep-learning-based learning models trained to restore original images from pairs of de-identified and original images. Conversely, the D2 GAN method, due to its generation of blurred images, may pose less risk, but it is not entirely risk free. Such analyses will help identify vulnerabilities in de-identification technologies and will lead to the development of more robust personal-data protection mechanisms.