Willem Verheyen
ABSTRACT
Thresholds play a crucial role in state-of-the-art biometric authentication systems as they determine the level of confidence required for a match between the presented biometric sample (e.g., facial image) and the stored reference template. These thresholds help balance security and convenience in biometric authentication. They are typically determined a priori based on some test data, and then fixed during system operation after deployment, and hence the same for all users. In this doctoral research, we investigate a.o. attacks against static thresholds by exploiting the non-uniform distributions of biometric characteristics, and research an extensible middleware solution for adaptive thresholding to offer the same level of security despite individual and demographic differences in biometric modalities. The ultimate goal of this study is to adjust state-of-the-art solutions of biometric authentication systems to dynamically adapt the threshold for enhanced security, robustness, and fairness. It goes without saying that incorporating dynamic adaptation within a distributed deployment environment introduces a new potential point of vulnerability in the attack surface. The projected middleware must duly address this critical issue.
- Bharat Bohara. 2020. Adaptive Threshold for Better Performance of the Recognition and Re-identification Models. CoRR abs/2012.14305 (2020). arXiv:2012.14305 https://arxiv.org/abs/2012.14305Google Scholar
- Hsin-Rung Chou, Jia-Hong Lee, Yi-Ming Chan, and Chu-Song Chen. 2019. Data-Specific Adaptive Threshold for Face Recognition and Authentication. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). 153--156.Google Scholar
- Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, and Christos Diou. 2023. Towards Fair Face Verification: An In-depth Analysis of Demographic Biases. arXiv preprint arXiv:2307.10011 (2023).Google Scholar
- Abdallah Hussein Sham, Kadir Aktas, Davit Rizhinashvili, Danila Kuklianov, Fatih Alisinanoglu, Ikechukwu Ofodile, Cagri Ozcinar, and Gholamreza Anbarjafari. 2023. Ethical AI in facial expression analysis: Racial bias. Signal, Image and Video Processing 17, 2 (2023), 399--406.Google ScholarCross Ref
- Willem Verheyen, Tim Van Hamme, Sander Joos, Davy Preuveneers, and Wouter Joosen. 2023. Beware the Doppelgänger: Attacks against Adaptive Thresholds in Facial Recognition Systems. In Proceedings of the 18th International Conference on Availability, Reliability and Security (Benevento, Italy) (ARES '23). Association for Computing Machinery, New York, NY, USA, Article 18, 11 pages.Google ScholarDigital Library
Index Terms
- Adaptive Thresholding for Fair and Robust Biometric Authentication
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