一种采用多保险库混淆模板的安全隐私集成匹配器
A secure and private ensemble matcher using multi-vault obfuscated templates
AI总结：
针对生物识别模板的安全与隐私需求，提出基于GAN生成合成人脸作为干扰点的多保险库混淆方法，在多个数据集上保持高精度的同时显著提升安全性。
摘要：
生成式AI凭借在数据生成方面前所未有的真实感、多样性与效率，革新了现代机器学习领域。这项技术在生物识别领域蕴含巨大潜力，可用于保护敏感的个人身份信息。鉴于生物识别样本具有不可撤销性，且隐私问题日益凸显，生物识别模板安全与安全匹配成为现代生物识别系统最受追捧的特性之一。本文提出一种利用生成式AI增强生物识别模板安全性的新型混淆方法。我们的方法将生成对抗网络（GAN）生成的合成人脸图像用作安全保险库系统中的“随机干扰点”。该方法从原始模板生成n个子模板，每个子模板均用m个GAN干扰点进行混淆。在验证阶段，从每个保险库中检索与生物识别查询最接近的s个向量，将它们组合生成哈希值，再与存储的哈希值进行比对。由此，我们的方法通过使用GAN生成的合成图像，在训练和部署阶段均能保护用户身份。我们使用AT&T、GT和LFW人脸数据集对该协议进行了测试，其ROC曲线下面积分别达到0.99、0.99和0.90。结果表明，所提方法在显著提升安全性与隐私性的同时，能保持与无保护模板方法相当的高精度与合理的计算复杂度，凸显了生成式AI在开发生物识别系统主动防御策略方面的潜力。
英文摘要：
Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation. This technology holds immense potential for biometrics, including for securing sensitive and personally identifiable information. Given the irrevocability of biometric samples and mounting privacy concerns, biometric template security and secure matching are among the most sought-after features of modern biometric systems. This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security. Our approach utilizes synthetic facial images generated by a Generative Adversarial Network (GAN) as "random chaff points" within a secure vault system. Our method creates n sub-templates from the original template, each obfuscated with m GAN chaff points. During verification, s closest vectors to the biometric query are retrieved from each vault and combined to generate hash values, which are then compared with the stored hash value. Thus, our method safeguards user identities during the training and deployment phases by employing the GAN-generated synthetic images. Our protocol was tested using the AT&T, GT, and LFW face datasets, achieving ROC areas under the curve of 0.99, 0.99, and 0.90, respectively. Our results demonstrate that the proposed method can maintain high accuracy and reasonable computational complexity comparable to those unprotected template methods while significantly enhancing security and privacy, underscoring the potential of Generative AI in developing proactive defensive strategies for biometric systems.
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。
