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