Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality
Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality
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DOI：
10.1109/vr50410.2021.00035
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发表时间：
2021-03
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影响因子：
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通讯作者：
Robert Miller;N. Banerjee;Sean Banerjee
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文献类型：
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作者：
Robert Miller;N. Banerjee;Sean Banerjee
在本文中，我们提供了一种方法，在虚拟现实（VR）环境中使用行为生物识别技术来执行跨系统的高保证的用户身份验证。VR目前正在被探索作为一种关键工具，以确保教育，医疗保健和个人理财等基本服务的无缝交付，同时使用户能够在家庭环境中工作。由于所产生的个人数据的敏感性，用于基本服务的VR应用程序需要提供安全的访问。传统的PIN或基于密码的凭证可能被恶意冒名顶替者破坏，或者被VR系统的预期用户交给同盟者以帮助预期用户完成任务，例如，检查或常规理疗当用户在不同的VR系统上提供注册和使用时间数据时，使用VR中用户的行为作为生物特征签名的现有方法失败。我们使用Siamese神经网络来学习距离函数，该函数表征不同VR系统对提供的数据之间的系统差异。我们的方法提供了平均等错误率（EER）范围从1.38%到3.86%的认证使用基准数据集，其中包括41个用户执行一个球投掷任务与3个VR系统-一个Oculus Quest，HTC Vive和HTC Vive宇宙。为了与VR生物识别技术中的先前方法进行比较，我们还获得了识别任务的平均精度，其中给定使用时VR系统中的输入用户轨迹，我们使用暹罗网络将注册VR系统中最匹配的轨迹作为标签返回给用户。我们报告的识别结果范围从87.82%到98.53%，与现有的方法相比，平均提高了29.78%±8.58%和30.78%±3.68%，这些方法分别在注册数据集上使用通用距离匹配和完全卷积网络。
In this paper, we provide an approach on using behavioral biometrics to perform cross-system high-assurance authentication of users in virtual reality (VR) environments. VR is currently being explored as a critical tool to ensure seamless delivery of essential services, such as education, healthcare, and personal finance, while enabling users to work from home environments. Due to the sensitive nature of personal data generated, VR applications for essential services need to provide secure access. Traditional PIN or password-based credentials can be breached by malicious impostors, or be handed over by an intended user of a VR system to a confederate to assist the intended user in completing a task, e.g., an exam or a physical therapy routine. Existing approaches that use the behavior of the user in VR as a biometric signature fail when users provide enrollment and use-time data on different VR systems. We use Siamese neural networks to learn a distance function that characterizes the systematic differences between data provided across pairs of dissimilar VR systems. Our approach provides average equal error rates (EERs) ranging from 1.38% to 3.86% for authentication using a benchmark dataset that consists of 41 users performing a ball-throwing task with 3 VR systems-an Oculus Quest, an HTC Vive, and an HTC Vive Cosmos. To compare to prior approaches in VR biometrics, we also obtain average accuracies for the task of identification, where given an input user's trajectory in a use-time VR system, we use Siamese networks to return the user with the top matching trajectory in an enrollment VR system as the label. We report identification results ranging from 87.82% to 98.53% with average improvements of 29.78%±8.58% and 30.78%±3.68% over existing approaches that use generic distance matching and fully convolutional networks on the enrollment dataset respectively.