# Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning

arXiv: https://arxiv.org/abs/2012.13293 · HTML: https://mlsanity.com/paper/2012.13293/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (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)
- [Securing Face and Fingerprint Templates in Humanitarian Biometric Systems](https://mlsanity.com/paper/2508.18415/) (2025)
- [Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models](https://mlsanity.com/paper/2209.11020/) (2022)

## Abstract (arXiv)

In this work, we study the protection that fuzzy commitments offer when they are applied to facial images, processed by the state of the art deep learning facial recognition systems. We show that while these systems are capable of producing great accuracy, they produce templates of too little entropy. As a result, we present a reconstruction attack that takes a protected template, and reconstructs a facial image. The reconstructed facial images greatly resemble the original ones. In the simplest attack scenario, more than 78% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FAR). Even in the "hardest" settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.