SaTC: CORE: Small: RUI: Leveraging Movement, Posture, and Anthropometric Contexts to Strengthen the Security of Mobile Biometrics

现代智能手机定期存储和访问大量的个人数据，如电子邮件、照片、视频和银行信息。因此，确保这些武器的安全至关重要。用户身份验证是确保智能手机安全的关键一步。然而，诸如图形密码、个人识别码（PIN）和指纹扫描的当前用户认证机制提供有限的安全性，并且在智能手机已经解锁之后无效。为了解决这些问题，该项目开发了持续的身份验证机制，该机制依赖于行为线索来确定智能手机是否被其所有者使用。该项目将导致（1）在真实姿势、运动和人体测量条件下对移动的行为生物特征的新的基础理解，以及（2）区分合法行为特征和伪造行为的新技术。该项目将系统地量化姿势、运动和人体测量变量对迄今未充分研究的受试人群的行为生物特征的影响，比如老年人和帕金森病患者。为此，该项目涉及分析和传播在两种环境中收集的数据集：（1）在实验室环境中收集的细粒度三维运动捕捉数据，以及（2）在用户日常环境中长达12个月的真实智能手机传感器数据。该项目预计将产生新的行为认证技术，通过适应上下文和行为的漂移，在现实条件下实现更低的错误率。 此外，该项目旨在量化行为生物特征对伪造攻击的敏感性，并引入依赖于上下文信息的新颖活性检测技术，以及轻量级和不显眼的用户挑战，以减轻这些攻击。研究人员将对多达150个主题的这些技术进行评估，并将以数据集、演示文稿、出版物和代码的形式分享该项目的结果。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Modern smartphones regularly store and access large amounts of personal data, such as e-mails, photos, videos, and banking information. For this reason, securing them is of paramount importance. User authentication is a crucial step towards securing a smartphone. However, current user authentication mechanisms such as graphical passwords, Personal Identification Numbers (PINs), and fingerprint scans offer limited security, and are ineffective after the smartphone has been unlocked. To address these issues, this project develops continuous authentication mechanisms that rely on behavioral cues to determine whether the smartphone is being used by its owner. This project will result in (1) new foundational understanding of mobile behavioral biometrics under realistic posture, movement, and anthropometric conditions, and (2) new techniques to distinguish legitimate behavioral traits from forgeries.The project will systematically quantify the impact of posture, movement, and anthropometric variables on behavioral biometric traits in hitherto understudied subject populations, such as older adults and people with Parkinson's disease. To this end, the project involves the analysis and dissemination of datasets collected in two settings: (1) fine-grained 3-dimensional motion capture data collected in a laboratory setting, and (2) real-world smartphone sensor data captured over a period of up to 12 months in the users' everyday environment. The project is expected to result in new behavioral authentication techniques that achieve lower error rates under realistic conditions by adapting to drifts in contexts and behaviors. Additionally, this project seeks to quantify the susceptibility of behavioral biometric traits to forgery attacks, and introduce novel liveness detection techniques that rely on contextual information, as well as lightweight and unobtrusive user challenges, to mitigate these attacks. The investigators will evaluate these techniques on up to 150 subjects, and will share the results of this project in the form of datasets, presentations, publications, and code.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.