In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art method in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions.

翻译：近年来，个人数据的日益可得引发了对隐私与安全的担忧。应对这些担忧的关键流程之一便是数据匿名化，其旨在保护个人隐私并防止敏感信息泄露。本研究聚焦于面部匿名化的重要性。为此，我们提出GANonymization这一新颖的人脸匿名化框架，该框架具备保留面部表情的能力。我们的方法基于人脸的高层表征，通过生成对抗网络（GAN）将其合成为匿名化版本。通过评估该方法在移除可识别人脸属性以提升特定个人面部匿名性方面的表现，对其有效性进行了评估。此外，在多个情感识别数据集上对保留面部表情的性能进行了评估，并在大多数字段中超越了现有最优方法。最后，我们分析了该方法移除各类面部特征（如饰品、发色等）的能力，结果表明其在移除这些属性方面表现可靠。我们的研究结论表明，GANonymization是一种兼顾面部表情保留与匿名化的有前景的方法。