# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 2 Related Work
### De-Identification  
De-identification primarily aims at privacy protection. It involves transforming or encrypting the original image to protect it from recognition systems, even if the given image appears identical, thereby minimizing identity information leakage due to the image. In [9], a framework for facial-image de-identification through adversarial perturbations in feature space was proposed, demonstrating the conversion of original images to substitute images. Another study with similar objectives was [10], which proposed a natural way to protect identity through strong 3D prior information and delicate generation design by using a \"divide and conquer\" strategy to train a GAN with adjusted loss to hide 3D separated identity codes and preserve image utility. And study [11] protected identity information by encrypting and decrypting facial-identity information in latent space based on the StyleGAN2 [12] generator. The advantage of this method is that it provides strong privacy protection by transforming an existing image into an alternative image while remaining cosmetically identical to the original image. The disadvantage is that adversarial transformations may only partially bypass the recognition system in certain situations. This technique benefits image sharing in online spaces where privacy is essential. The authors in [13] proposed a facial de-identification system using Conditional GAN [14] to generate realistic faces, satisfying various facial features (e.g., gender, hair color, and facial shape). A similar study by [PERSON] et al. [15], used a facial-attribute-transfer model to change the face while maintaining the natural appearance of the de-identified face through an encoder and decoder neural network. Additionally, study [16] proposed a model that uses a StyleGAN [17] to blend the styles or features of the target face and the auxiliary face into a natural-looking face. It has the advantage of vigorously protecting user identity while allowing re-identification back to the original facial image. However, it can be limited in generating highly controlled features and requires significant computational resources to train the model. Applicable scenarios include protecting personal identities in public databases, such as social media. In [18], a method for generating adversarial identity masks to hide identity from recognition systems was proposed. The approach presented there protects faces from facial recognition systems by overlaying a mask on the image, ensuring no change in the image's appearance. This method is particularly suitable for security-critical environments where the identity of the individual needs to be protected. Similarly, the authors of [19] proposed a method to hide identity from facial-recognition systems through a diffusion model. This technique has the advantage of effectively anonymizing identifying information while maintaining the natural appearance of the image, among other things. Applicable scenarios include various digital platforms that require the protection of an individual's identity, such as photo sharing. De-identification research has also been conducted in the medical field. In [20], a method was presented that combines the local-differential-privacy algorithm with GLOW, a flow-based deep generative model, to protect personal identification information in medical images. This method has the advantage of enhancing patient privacy in healthcare, but consideration should be given to its scalability in other areas that require a high degree of data protection.