Privacy concerns in the modern digital age have prompted researchers to develop techniques that allow users to selectively suppress certain information in collected data while allowing for other information to be extracted. In this regard, Semi-Adversarial Networks (SAN) have recently emerged as a method for imparting soft-biometric privacy to face images. SAN enables modifications of input face images so that the resulting face images can still be reliably used by arbitrary conventional face matchers for recognition purposes, while attribute classifiers, such as gender classifiers, are confounded. However, the generalizability of SANs across arbitrary gender classifiers has remained an open concern. In this work, we propose a new method, FlowSAN, for allowing SANs to generalize to multiple unseen gender classifiers. We propose combining a diverse set of SAN models to compensate each other's weaknesses, thereby, forming a robust model with improved generalization capability. Extensive experiments using different unseen gender classifiers and face matchers demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.

本文介绍了半对抗网络（SAN）的应用，以加强面部图像软生物特征上的隐私保护。作者提出了一种新的方法FlowSAN，以解决SAN在多个未见过的性别分类器中的泛化问题。该方法通过将不同的SAN模型组合在一起，以提升模型的泛化能力。作者进行了大量实验，证明了该方法的有效性。

FlowSAN：隐私增强的半对抗网络以困惑任意基于面孔的性别分类器