Abstract
The widespread use of deep learning face recognition raises several security concerns. Although prior works point at existing vulnerabilities, DNN backdoor attacks against real-life, unconstrained systems dealing with images captured in the wild remain a blind spot of the literature. This paper conducts the first system-level study of backdoors in deep learning-based face recognition systems. This paper yields four contributions by exploring the feasibility of DNN backdoors on these pipelines in a holistic fashion. We demonstrate for the first time two backdoor attacks on the face detection task: face generation and face landmark shift attacks. We then show that face feature extractors trained with large margin losses also fall victim to backdoor attacks. Combining our models, we then show using 20 possible pipeline configurations and 15 attack cases that a single backdoor enables an attacker to bypass the entire function of a system. Finally, we provide stakeholders with several best practices and countermeasures.
Abstract (translated)
深度学习面部识别的广泛应用引发了几项安全问题。尽管先前的研究指出了现有的漏洞，但针对实际环境中捕获图像的真实无约束系统进行DNN后门攻击仍然是文献中的盲点。本文首次对基于深度学习的面部识别系统的后门进行了系统级研究。本文通过全面探索这些管道中DNN后门的可能性，在四个方面做出了贡献。我们首次展示了在面部检测任务上的两个后门攻击：面部生成和面部关键点偏移攻击。接着，我们还表明使用大边距损失训练的面部特征提取器同样容易受到后门攻击的影响。结合我们的模型，我们通过20种可能的管道配置和15个攻击案例展示了一个单一的后门就足以使攻击者绕过整个系统的功能。最后，本文为利益相关者提供了几种最佳实践和对策。
URL
https://arxiv.org/abs/2507.01607
