通过变形优化基于人脸的一次性生物特征的密钥选择
Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing
AI总结：
针对人脸识别易受对抗性攻击且隐私增强方案忽视该威胁的问题，提出多种信号层密钥选择策略，增强可取消生物特征方案安全性，并将实用阈值下攻击成功率降至约5.0%。
摘要：
如今，人脸识别系统仍然容易受到对抗性攻击。这些攻击的形式从对输入图像的简单扰动，到修改识别模型参数以冒充授权主体，不一而足。所谓的隐私增强人脸识别系统主要是为了保护存储的生物特征参考数据（即模板）而开发的。在现有文献中，隐私增强人脸识别方法仅关注模板层面的传统安全威胁，而忽视了与对抗性攻击相关的日益增长的担忧。迄今为止，很少有工作提供在保持模板层面高安全性的同时保护人脸识别免受对抗性攻击的机制。在本文中，我们提出了不同的密钥选择策略，以提高一种在信号层面运行的具有竞争力的可取消方案的安全性。实验结果表明，某些基于信号层面密钥选择的策略能够在最安全阈值下完全阻断基于迭代优化的对抗性攻击，而在最实用阈值下，攻击成功概率可降低至约5.0%。
英文摘要：
Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric reference data, i.e. templates. In the literature, privacy-enhancing facial recognition approaches have focused solely on conventional security threats at the template level, ignoring the growing concern related to adversarial attacks. Up to now, few works have provided mechanisms to protect face recognition against adversarial attacks while maintaining high security at the template level. In this paper, we propose different key selection strategies to improve the security of a competitive cancelable scheme operating at the signal level. Experimental results show that certain strategies based on signal-level key selection can lead to complete blocking of the adversarial attack based on an iterative optimization for the most secure threshold, while for the most practical threshold, the attack success chance can be decreased to approximately 5.0%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
