Make Identity Unextractable yet Perceptible: Synthesis-Based Privacy Protection for Subject Faces in Photos

Abstract

Deep learning-based face recognition (FR) technology exacerbates privacy concerns in photo sharing. In response, the research community developed a suite of anti-FR methods to block identity extraction by unauthorized FR systems. Benefiting from quasi-imperceptible alteration, perturbation-based methods are well-suited for privacy protection of subject faces in photos, as they allow familiar persons to recognize subjects via naked eyes. However, we reveal that perturbation-based methods provide a false sense of privacy through theoretical analysis and experimental validation. Therefore, new alternative solutions should be found to protect subject faces. In this paper, we explore synthesis-based methods as a promising solution, whose challenge is to enable familiar persons to recognize subjects. To solve the challenge, we present a key insight: In most photo sharing scenarios, familiar persons recognize subjects through identity perception rather than meticulous face analysis. Based on the insight, we propose the first synthesis-based method dedicated to subject faces, i.e., PerceptFace, which can make identity unextractable yet perceptible. To enhance identity perception, a new perceptual similarity loss is designed for faces, reducing the alteration in regions of high sensitivity to human vision. As a synthesis-based method, PerceptFace can inherently provide reliable identity protection. Meanwhile, out of the confine of meticulous face analysis, PerceptFace focuses on identity perception from a more practical scenario, which is also enhanced by the designed perceptual similarity loss. Sufficient experiments show that PerceptFace achieves a superior trade-off between identity protection and identity perception compared to existing methods. We provide a public API of PerceptFace and believe that it has great potential to become a practical anti-FR tool.