Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage
This work exposes a critical vulnerability in IronMask, compromising renewability for biometric security systems, and is incremental as it builds on known attack methods.
The authors tackled the security of IronMask, a biometric template protection scheme, by developing a Probabilistic Linear Regression Attack that exploits linearity in error-correcting codes to recover original templates from multiple protected ones, achieving successful recovery in acceptable time and storage as verified in experiments.
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.