Details

Autor(en) / Beteiligte

• Wang, Yong
• Yang, Tianyu
• Wang, Chunxiao
• Li, Feng
• Hu, Pengfei
• Shen, Yiran

Titel

BudsAuth: Toward Gesture-Wise Continuous User Authentication Through Earbuds Vibration Sensing

Ist Teil von

• IEEE internet of things journal, 2024-06, Vol.11 (12), p.22007-22020

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2024

Quelle

IEEE Xplore

Beschreibungen/Notizen

• The surge in popularity of wireless headphones, particularly wireless earbuds, as smart wearables, has been notable in recent years. These devices, empowered by artificial intelligence (AI), are broadening their utility in areas such as speech recognition, augmented reality, pose recognition, and health care monitoring, thereby enriching user experiences through novel interactive interfaces driven by embedded sensors. However, the widespread adoption of wireless earbuds has spurred concerns regarding security and privacy, necessitating robust bespoke security measures. Despite the miniaturization of mobile chips enabling the integration of sophisticated algorithms into smart wearables, the research and industrial communities have yet to accord adequate attention to earbud security. This article focuses on empowering wireless earbuds to authenticate their legitimate users, tackling the challenges associated with conventional authentication methods. Instead of relying on input interface authentication methods like PIN or lock patterns, this research delves into leveraging inertial measurement unit (IMU) data collected during interactions with devices to extract novel biometric features, presenting an alternative approach that nonetheless confronts challenges related to signal capture and interference. Consequently, we propose and design BudsAuth, an implicit user authentication framework that harnesses built-in IMU sensors in smart earbuds to capture vibration signals induced by on-face touching interactions with the earbuds. These vibrations are utilized to deliver continuous and implicit user authentication with high precision and compatibility across various earbud models. Extensive evaluation demonstrates BudsAuth's capability to achieve an equal error rate (EER) of 0.0003, representing an approximate 99.97% accuracy with seven consecutive samples of interactive gestures for implicit authentication.