Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage

TL;DR

This paper introduces a novel probabilistic linear regression attack on IronMask biometric protection, successfully recovering original templates from multiple protected templates and demonstrating robustness in noisy conditions.

Contribution

It presents the first effective attack on IronMask's security notion of renewability, exploiting the linearity of the error correcting code used in the scheme.

Findings

The attack successfully recovers original templates in acceptable time.

The attack remains effective in noisy environments.

Two mitigation strategies are proposed to defend against this attack.

Abstract

Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise an attack on IronMask targeting on the security notion of renewability. Our attack, termed as Probabilistic Linear Regression Attack, utilizes the linearity of underlying used error correcting code. This attack is the first algorithm to successfully recover the original template when getting multiple protected templates in acceptable time and requirement of storage. We implement experiments on IronMask applied to protect ArcFace that well verify the validity of our attacks. Furthermore, we carry out experiments in noisy environments and confirm that our attacks are still applicable. Finally, we put forward two strategies to mitigate this type of attacks.