Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing
Daile Osorio-Roig Mahdi Ghafourian Christian Rathgeb Ruben Vera-Rodriguez Christoph Busch Julian Fierrez
Nowadays, facial recognition systems are still vulnerable to adversarial
attacks. These attacks vary from simple perturbations of the input image to
modifying the parameters of the recognition model to impersonate an authorised
subject. So-called privacy-enhancing facial recognition systems have been
mostly developed to provide protection of stored biometric reference data, i.e.
templates. In the literature, privacy-enhancing facial recognition approaches
have focused solely on conventional security threats at the template level,
ignoring the growing concern related to adversarial attacks. Up to now, few
works have provided mechanisms to protect face recognition against adversarial
attacks while maintaining high security at the template level. In this paper,
we propose different key selection strategies to improve the security of a
competitive cancelable scheme operating at the signal level. Experimental
results show that certain strategies based on signal-level key selection can
lead to complete blocking of the adversarial attack based on an iterative
optimization for the most secure threshold, while for the most practical
threshold, the attack success chance can be decreased to approximately 5.0%.