FaceCloak: Learning to Protect Face Templates

Sudipta Banerjee, Anubhav Jain, Chinmay Hegde, Nasir Memon
4/8/2025
cs.CV

Official code: sudban3089/FaceCloak (1 star, last commit 2025-05-06).

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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Code Implementations(1)

sudban3089/FaceCloakOfficial100%

Protecting Against Face Template Inversion Attacks

11PythonApr 7, 20251 years ago

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Cite this paper

@article{banerjee2025facecloak,
  title  = {FaceCloak: Learning to Protect Face Templates},
  author = {Sudipta Banerjee and Anubhav Jain and Chinmay Hegde and Nasir Memon},
  year   = {2025},
  eprint = {2504.06131},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.06131v2}
}

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