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

arXiv: https://arxiv.org/abs/2310.02997 · HTML: https://mlsanity.com/paper/2310.02997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Security Evaluation of Pattern Classifiers under Attack](https://mlsanity.com/paper/1709.00609/) (2017)
- [Fast Geometrically-Perturbed Adversarial Faces](https://mlsanity.com/paper/1809.08999/) (2018)
- [A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing](https://mlsanity.com/paper/1910.01389/) (2019)
- [Interpretable security analysis of cancellable biometrics using constrained-optimized similarity-based attack](https://mlsanity.com/paper/2006.13051/) (2020)
- [OTB-morph: One-Time Biometrics via Morphing](https://mlsanity.com/paper/2302.09053/) (2023)
- [Deep Learning in the Field of Biometric Template Protection: An Overview](https://mlsanity.com/paper/2303.02715/) (2023)

## Abstract (arXiv)

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%.