# FaceCloak: Learning to Protect Face Templates

arXiv: https://arxiv.org/abs/2504.06131 · HTML: https://mlsanity.com/paper/2504.06131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Arc2Face: A Foundation Model for ID-Consistent Human Faces](https://mlsanity.com/paper/2403.11641/) (2024)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [Orthogonal Projection Loss](https://mlsanity.com/paper/2103.14021/) (2021)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [HERS: Homomorphically Encrypted Representation Search](https://mlsanity.com/paper/2003.12197/) (2020)
- [Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing](https://mlsanity.com/paper/1703.05455/) (2017)
- [Towards Protecting Face Embeddings in Mobile Face Verification Scenarios](https://mlsanity.com/paper/2110.00434/) (2021)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [General Framework to Evaluate Unlinkability in Biometric Template Protection Systems](https://mlsanity.com/paper/2311.04633/) (2023)
- [IronMask: Modular Architecture for Protecting Deep Face Template](https://mlsanity.com/paper/2104.02239/) (2021)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron](https://mlsanity.com/paper/2204.11054/) (2022)

## Abstract (arXiv)

Generative models can reconstruct face images from encoded representations (templates) bearing remarkable likeness to the original face, raising security and privacy concerns. We present textscFaceCloak, 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. textscFaceCloak-based matching is extremely fast (inference time =0.28 ms) and light (0.57 MB). We have released our hrefhttps://github.com/sudban3089/FaceCloak.gitcode for reproducible research.