The widespread deployment of Deep Learning-based Face Recognition Systems raises many security concerns. While prior research has identified backdoor vulnerabilities on isolated components, Backdoor Attacks on real-world, unconstrained pipelines remain underexplored. This SoK paper presents the first comprehensive system-level analysis and measurement of the impact of Backdoor Attacks on fully-fledged Face Recognition Systems. We combine the existing Supervised Learning backdoor literature targeting face detectors, face antispoofing, and face feature extractors to demonstrate a system-level vulnerability. By analyzing 20 pipeline configurations and 15 attack scenarios in a holistic manner, we reveal that an attacker only needs a single backdoored model to compromise an entire Face Recognition System. Finally, we discuss the impact of such attacks and propose best practices and countermeasures for stakeholders.
@article{arxiv.2507.01607,
title = {SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems},
author = {Quentin Le Roux and Yannick Teglia and Teddy Furon and Philippe Loubet-Moundi and Eric Bourbao},
journal= {arXiv preprint arXiv:2507.01607},
year = {2026}
}
Comments
This work has been accepted for publication at the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). The final version will be available on IEEE Xplore