FaceCloak: Learning to Protect Face Templates

• URL: http://arxiv.org/abs/2504.06131v2
• Date: Mon, 27 Oct 2025 18:41:08 GMT
• Title: FaceCloak: Learning to Protect Face Templates
• Authors: Sudipta Banerjee, Anubhav Jain, Chinmay Hegde, Nasir Memon,
• Abstract summary: We present textscFaceCloak, a neural network framework that protects face templates by generating smart, renewable binary cloaks.<n>textscFaceCloak-based matching is extremely fast (inference time =0.28 ms) and light (0.57 MB)
• Score: 14.52783380280229
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generative models can reconstruct face images from encoded representations (templates) bearing remarkable likeness to the original face, raising security and privacy concerns. We present \textsc{FaceCloak}, a neural network framework that protects face templates by generating smart, renewable binary cloaks. Our method proactively thwarts inversion attacks by cloaking face templates with unique disruptors synthesized from a single face template on the fly while provably retaining biometric utility and unlinkability. Our cloaked templates can suppress sensitive attributes while generalizing to novel feature extraction schemes and outperform leading baselines in terms of biometric matching and resiliency to reconstruction attacks. \textsc{FaceCloak}-based matching is extremely fast (inference time =0.28 ms) and light (0.57 MB). We have released our \href{https://github.com/sudban3089/FaceCloak.git}{code} for reproducible research.

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