Face recognition service providers protect face privacy by extracting compact
and discriminative facial features (representations) from images, and storing
the facial features for real-time recognition. However, such features can still
be exploited to recover the appearance of the original face by building a
reconstruction network. Although several privacy-preserving methods have been
proposed, the enhancement of face privacy protection is at the expense of
accuracy degradation. In this paper, we propose an adversarial features-based
face privacy protection (AdvFace) approach to generate privacy-preserving
adversarial features, which can disrupt the mapping from adversarial features
to facial images to defend against reconstruction attacks. To this end, we
design a shadow model which simulates the attackers' behavior to capture the
mapping function from facial features to images and generate adversarial latent
noise to disrupt the mapping. The adversarial features rather than the original
features are stored in the server's database to prevent leaked features from
exposing facial information. Moreover, the AdvFace requires no changes to the
face recognition network and can be implemented as a privacy-enhancing plugin
in deployed face recognition systems. Extensive experimental results
demonstrate that AdvFace outperforms the state-of-the-art face
privacy-preserving methods in defending against reconstruction attacks while
maintaining face recognition accuracy.

本研究提出了一种基于对抗特征的面部隐私保护 (AdvFace) 方法，通过在对手模型中生成对抗性的潜在噪声来破坏从对抗性特征到面部图像的映射，生成隐私保护的对抗性特征来防御面部重建攻击。实验表明，AdvFace 在维护面部识别准确性的同时，比目前最先进的面部隐私保护方法更有效地防御重建攻击。