# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 3 Proposed Methods
### Face-De-Identification Model using D2 GAN
Additionally, the Original vs. De-identified Discriminator (\(D_{OD}\)) is trained to accurately distinguish between original and de-identified face images. This discriminator ensures that the de-identification process effectively obscures personal identifying features while retaining the essential characteristics of the original image. Finally, Equation (6) represents the overall loss of the generator responsible for producing the de-identified image. An input image is represented by \(x_{i}\), and \(y_{i}\) represents the input image blurred with a Gaussian filter. This loss utilizes a combination of Binary Cross-Entropy (BCE) Loss and Logistic Loss for the generator. The BCE Loss measures the discrepancy between the discriminator's prediction and the actual label, indicating how effectively the generator deceives the discriminator into perceiving the generated images as real. Meanwhile, the Logistic Loss, applied through a sigmoid activation function (\(\sigma\)), further refines the model's predictions, minimizing the difference between the predicted probabilities and the actual binary outcomes. Together, these two losses enhance the generator's ability to produce realistic and convincing de-identified images. Additionally, Identity Loss is considered, which calculates the L1 distance between the generated de-identified face and the original face, thus preserving essential characteristics while enhancing privacy. The final loss is calculated by incorporating each \(\lambda\) ratio. The feature-similarity loss is denoted by \(L_{FS}\) and uses Equation (3), as used in the feature-inversion model. \[ \begin{split} L_{G_{total}}=\lambda_{GAN}\times\left(-\sum_{i}[y _{i}\times\log\sigma(G(x_{i}))+(1-y_{i})\times\log(1-\sigma(G(x_{i})))]\right) \\ +\lambda_{Identity}\times\frac{1}{N}\sum_{i}\lvert G(x_{i})-x_{i }\rvert+\lambda_{FS}L_{FS}\end{split}  \tag{6}\]  
The design of our model generates images in a manner akin to Gaussian blurring, thereby maintaining the feature similarity of the overall image without significant compromise. This balanced approach ensures enhanced privacy while preserving the image's intended utility. The primary rationale for this configuration is to achieve an optimal balance between practicality and the necessity for privacy protection. We anticipate that this strategy will deliver substantial value in real-world applications. Similar to the models proposed in Section 3.2, we use several evaluation metrics to evaluate the performance of de-identified images and videos, including Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and cosine similarity. For comparative analysis, we also compare the performance using query images and videos with de-identification applied to them to the performance using the original dataset without such processing. This comparative analysis provides a deeper understanding of how the proposed models effectively preserve the key characteristics of the data required for the clone-detection task while enhancing privacy.