首次在合理时间和存储下，从多个受保护模板恢复原始生物特征数据
Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage
- 利用纠错码线性特性，设计概率线性回归攻击
- 在多份保护模板下成功恢复原始人脸特征，实验验证有效
- 适用于噪声环境，为IronMask提供防御策略
生物特征模板的保护是当前亟需解决的关键问题。IronMask在保持高识别性能的同时，能有效抵御现有已知攻击。本质上，IronMask是一种基于超球面的模糊承诺方案。本文针对其可更新性安全假设提出一种攻击，称为概率线性回归攻击，该方法利用底层纠错码的线性特性。这是首个在合理时间与存储开销下，成功从多个受保护模板中恢复原始模板的算法。我们在ArcFace上实施实验，验证了攻击的有效性。此外，在噪声环境下也证实该攻击依然适用。最后，提出了两种缓解此类攻击的策略。
原文摘要 · Abstract (English)
Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise an attack on IronMask targeting on the security notion of renewability. Our attack, termed as Probabilistic Linear Regression Attack, utilizes the linearity of underlying used error correcting code. This attack is the first algorithm to successfully recover the original template when getting multiple protected templates in acceptable time and requirement of storage. We implement experiments on IronMask applied to protect ArcFace that well verify the validity of our attacks. Furthermore, we carry out experiments in noisy environments and confirm that our attacks are still applicable. Finally, we put forward two strategies to mitigate this type of attacks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。