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Nowadays, facial recognition systems are still vulnerable to adversarialattacks. These attacks vary from simple perturbations of the input image tomodifying the parameters of the recognition model to impersonate an authorisedsubject. So-called privacy-enhancing facial recognition systems have beenmostly developed to provide protection of stored biometric reference data, i.e.templates. In the literature, privacy-enhancing facial recognition approacheshave focused solely on conventional security threats at the template level,ignoring the growing concern related to adversarial attacks. Up to now, fewworks have provided mechanisms to protect face recognition against adversarialattacks while maintaining high security at the template level. In this paper,we propose different key selection strategies to improve the security of acompetitive cancelable scheme operating at the signal level. Experimentalresults show that certain strategies based on signal-level key selection canlead to complete blocking of the adversarial attack based on an iterativeoptimization for the most secure threshold, while for the most practicalthreshold, the attack success chance can be decreased to approximately 5.0%.

Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing