International Association
for Cryptologic Research

Protection of biometric templates is a critical and urgent area of focus. \textbf{IronMask} demonstrates superior 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 \textbf{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 \textbf{IronMask} applied to protect \textbf{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 discuss two strategies to mitigate this type of attacks.