# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 3 Proposed Methods
### Face De-Identification Model Using Feature Inversion
The generated de-identified face image and the whole image are optimized through several loss functions to maintain similarity to the original, and this process finally minimizes the total loss, including the face-image-similarity loss, the face-region-similarity loss, and the whole-image-feature-similarity loss. \[L_{\text{RS}}=\frac{1}{N}\sum_{i=1}^{N}(O_{i}-D_{i})^{2} \tag{2}\]  
Equation (3) computes the cosine similarity of the features extracted from the original and de-identified full images, where \(M\) represents the number of dimensions of the feature vector, \(F_{O_{i}}\) is the feature vector of the original full image, and \(F_{D_{i}}\) is the feature vector of the de-identified image:  
\[L_{FS}=1-\left(\frac{1}{M}\sum_{j=1}^{M}\frac{F_{O_{j}}\cdot F_{D_{j}}}{\left\| F_{O_{j}}\right\|\left\|F_{D_{j}}\right\|}\right) \tag{3}\]  
A lower value of \(L_{RS}\) indicates a higher similarity between the original and the de-identified face. In our proposed model, to avoid similarity between the two faces, we set the value of \(\lambda_{RS}\) to be low during the learning process. Consequently, we aim for the features extracted from the original and de-identified images to exhibit similarity. Therefore, we set the value of \(\lambda_{FS}\) relatively high. Owing to this structure, the model's loss function enables the Deid Face Image Generator to train in a manner that ensures the feature vectors generated from the entire image and the de-identified face image are similar. At the same time, it reflects the similarity between the original and de-identified facial regions. Our proposed face de-identification using the feature-inversion model showcases its ability to maintain the utility of de-identified images while ensuring strong privacy protection. This method opens up promising avenues in the advancement of image-based privacy and security technologies. For the evaluation of the proposed model, we use several evaluation metrics to assess the performance of de-identified images and videos, including Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and cosine similarity. SSIM and PSNR are important for evaluating the visual similarity of facial regions before and after de-identification, while cosine similarity measures the similarity of features between the transformed image and the original image, to assess how effectively the model transformed the image to preserve privacy. For comparative analysis, this study compares performance using query images and videos to de-identification processing applied and performance using the original dataset without such processing. This allows us to quantitatively evaluate the impact of de-identification on image- and video-clone-detection tasks. Through experiments with the DISC21 and VCDB datasets, we show that even after de-identification, the model maintains its ability to detect clones while effectively protecting privacy. These evaluation methods and comparison criteria will help to provide a comprehensive understanding of how effectively models can protect privacy while maintaining the usefulness of the data.