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arXiv · 2507.01607

SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems

Quentin Le Roux·Yannick Teglia·Teddy Furon·Philippe Loubet-Moundi·Eric Bourbao
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Abstract

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.

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Quentin Le Roux, Yannick Teglia, Teddy Furon, Philippe Loubet-Moundi, Eric Bourbao. 2025-07-02. SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems. https://arxiv.org/abs/2507.01607

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