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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01607 |
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| _version_ | 1866912832509968384 |
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| author | Roux, Quentin Le Teglia, Yannick Furon, Teddy Loubet-Moundi, Philippe Bourbao, Eric |
| author_facet | Roux, Quentin Le Teglia, Yannick Furon, Teddy Loubet-Moundi, Philippe Bourbao, Eric |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems Roux, Quentin Le Teglia, Yannick Furon, Teddy Loubet-Moundi, Philippe Bourbao, Eric Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Machine Learning 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. |
| title | SoK: On the Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2507.01607 |