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This paper proposes a novel paradigm for facial privacy protection thatunifies multiple characteristics including anonymity, diversity, reversibilityand security within a single lightweight framework. We name it PRO-Face S,short for Privacy-preserving Reversible Obfuscation of Face images via Secureflow-based model. In the framework, an Invertible Neural Network (INN) isutilized to process the input image along with its pre-obfuscated form, andgenerate the privacy protected image that visually approximates to thepre-obfuscated one, thus ensuring privacy. The pre-obfuscation applied can bein diversified form with different strengths and styles specified by users.Along protection, a secret key is injected into the network such that theoriginal image can only be recovered from the protection image via the samemodel given the correct key provided. Two modes of image recovery are devisedto deal with malicious recovery attempts in different scenarios. Finally,extensive experiments conducted on three public image datasets demonstrate thesuperiority of the proposed framework over multiple state-of-the-artapproaches.

PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow