{"ID":2899140,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01607","arxiv_id":"2507.01607","title":"SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems","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.","short_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 syst...","url_abs":"https://arxiv.org/abs/2507.01607","url_pdf":"https://arxiv.org/pdf/2507.01607v5","authors":"[\"Quentin Le Roux\",\"Yannick Teglia\",\"Teddy Furon\",\"Philippe Loubet-Moundi\",\"Eric Bourbao\"]","published":"2025-07-02T11:21:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.CR\",\"cs.LG\"]","methods":"[]","has_code":false}