# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 1 Introduction  
The surge in media-sharing platforms has increased the distribution of unauthorized and explicit content, raising privacy concerns. Government agencies and media-sharing platforms create databases of harmful images and videos for content moderation. While current methods use large-scale datasets for content filtering, a challenge arises when using deep learning: using facial data raises ethical issues around privacy. Thus, there is a need for a novel approach that constructs training datasets by eliminating facial information, ensuring privacy preservation while minimizing information loss, and facilitating the development of robust models to address these concerns. To address this challenge, our study focuses on facial de-identification, with the aim of protecting privacy without compromising data utility. Traditional methods [1; 2; 3; 4; 5] typically use pixel-level transformations, such as average filters, median filters, or Gaussian filters, to obscure the facial regions. However, these pixel-level transformations often compromise the balance between preserving the utility of data and protecting individual privacy. While they effectively obscure facial features to address privacy concerns, this direct manipulation of pixel values can significantly degrade the quality of the dataset, resulting in a loss of critical-feature representation. This degradation not only diminishes the effectiveness of subsequent analytical models but also raises questions about the adequacy of privacy protection, as overly blurred or distorted images may still leave room for re-identification through advanced techniques or contextual information. We propose a deep-learning-based de-identification approach that preserves the features of input images while anonymizing facial regions in image- and video-copy-detection tasks. This paper focuses on facial de-identification, aiming for minimal data-information loss and for personal-information protection. To obscure facial regions, we employ (1) Feature Inversion [6; 7] and (2) D2 GAN [8] techniques: (1) The feature-inversion-based method emphasizes the contours of the original face. It uses features extracted from the intermediate layers of a Convolutional Neural Network (CNN) model, focusing on the facial region of the input image, to reconstruct the original input data. During reconstruction, features are extracted from both the original and de-identified facial images, and their similarity is integrated into the loss function. This allows the model to learn to generate images with similar features after de-identification. The feature-inversion method was selected due to its proficiency in reconstructing images with emphasis on contours and significant features, observed particularly in natural images, where it promotes feature representation based on contours. This aligns with the goal of preserving crucial elements while de-identifying faces. (2) The D2 GAN-based method blurs the face region. It uses a generator and two discriminators to de-identify the facial region. One discriminator distinguishes whether the generated face image is real or fake, and the other determines whether the generated face image is original or de-identified. D2 GAN was opted for due to its distinctive approach to handling the ambiguity in the direction of image transformation, common in typical GANs when presented with facial images. The dual-discriminator structure of D2 GAN effectively addresses this by directing the generation towards producing blurred facial images, which ensures a substantial degree of anonymity and maintains feature similarity. This approach also uses the same loss function as the feature-inversion method, enabling the model to learn to generate images with similar features after de-identification. Both models demonstrate high feature-similarity performance after de-identification, confirming that image features can be preserved without loss even through the de-identification process.