用GAN将人脸转为隐私图像，实现安全认证。
Generative Adversarial Networks Applied for Privacy Preservation in Biometric-Based Authentication and Identification
- 用GAN将人脸转换为花、鞋等视觉隐私图像
- 在转换后图像上训练分类器仍能准确认证
- 既保护隐私又保持系统可用性，适合生物识别场景
基于生物特征的认证系统正被广泛采用，但用户无法控制其数据使用方式，且存在数据泄露和滥用风险。本文提出一种基于生成对抗网络（GAN）的新型认证方法，通过GAN将人脸图像转换至视觉隐私域（如花朵或鞋子），并在转换后的图像上训练用于认证的分类器。实验表明，该方法对攻击具有鲁棒性，同时保留了足够的认证效用，实现了隐私保护与系统功能的平衡。
原文摘要 · Abstract (English)
Biometric-based authentication systems are getting broadly adopted in many areas. However, these systems do not allow participating users to influence the way their data is used. Furthermore, the data may leak and can be misused without the users' knowledge. In this paper, we propose a new authentication method that preserves the privacy of individuals and is based on a generative adversarial network (GAN). Concretely, we suggest using the GAN for translating images of faces to a visually private domain (e.g., flowers or shoes). Classifiers, which are used for authentication purposes, are then trained on the images from the visually private domain. Based on our experiments, the method is robust against attacks and still provides meaningful utility.
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