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Privacy concerns in the modern digital age have prompted researchers todevelop techniques that allow users to selectively suppress certain informationin collected data while allowing for other information to be extracted. In thisregard, Semi-Adversarial Networks (SAN) have recently emerged as a method forimparting soft-biometric privacy to face images. SAN enables modifications ofinput face images so that the resulting face images can still be reliably usedby arbitrary conventional face matchers for recognition purposes, whileattribute classifiers, such as gender classifiers, are confounded. However, thegeneralizability of SANs across arbitrary gender classifiers has remained anopen concern. In this work, we propose a new method, FlowSAN, for allowing SANsto generalize to multiple unseen gender classifiers. We propose combining adiverse set of SAN models to compensate each other's weaknesses, thereby,forming a robust model with improved generalization capability. Extensiveexperiments using different unseen gender classifiers and face matchersdemonstrate the efficacy of the proposed paradigm in imparting gender privacyto face images.

FlowSAN: Privacy-enhancing Semi-Adversarial Networks to Confound Arbitrary Face-based Gender Classifiers