EarDynamic

Author(s):  
Zi Wang ◽  
Sheng Tan ◽  
Linghan Zhang ◽  
Yili Ren ◽  
Zhi Wang ◽  
...  

Biometric-based authentication is gaining increasing attention for wearables and mobile applications. Meanwhile, the growing adoption of sensors in wearables also provides opportunities to capture novel wearable biometrics. In this work, we propose EarDynamic, an ear canal deformation based user authentication using in-ear wearables. EarDynamic provides continuous and passive user authentication and is transparent to users. It leverages ear canal deformation that combines the unique static geometry and dynamic motions of the ear canal when the user is speaking for authentication. It utilizes an acoustic sensing approach to capture the ear canal deformation with the built-in microphone and speaker of the in-ear wearable. Specifically, it first emits well-designed inaudible beep signals and records the reflected signals from the ear canal. It then analyzes the reflected signals and extracts fine-grained acoustic features that correspond to the ear canal deformation for user authentication. Our extensive experimental evaluation shows that EarDynamic can achieve a recall of 97.38% and an F1 score of 96.84%. Results also show that our system works well under different noisy environments with various daily activities.

Author(s):  
Yang Gao ◽  
Yincheng Jin ◽  
Jagmohan Chauhan ◽  
Seokmin Choi ◽  
Jiyang Li ◽  
...  

With the rapid growth of wearable computing and increasing demand for mobile authentication scenarios, voiceprint-based authentication has become one of the prevalent technologies and has already presented tremendous potentials to the public. However, it is vulnerable to voice spoofing attacks (e.g., replay attacks and synthetic voice attacks). To address this threat, we propose a new biometric authentication approach, named EarPrint, which aims to extend voiceprint and build a hidden and secure user authentication scheme on earphones. EarPrint builds on the speaking-induced body sound transmission from the throat to the ear canal, i.e., different users will have different body sound conduction patterns on both sides of ears. As the first exploratory study, extensive experiments on 23 subjects show the EarPrint is robust against ambient noises and body motions. EarPrint achieves an Equal Error Rate (EER) of 3.64% with 75 seconds enrollment data. We also evaluate the resilience of EarPrint against replay attacks. A major contribution of EarPrint is that it leverages two-level uniqueness, including the body sound conduction from the throat to the ear canal and the body asymmetry between the left and the right ears, taking advantage of earphones' paring form-factor. Compared with other mobile and wearable biometric modalities, EarPrint is a low-cost, accurate, and secure authentication solution for earphone users.


2021 ◽  
Author(s):  
Zi Wang ◽  
Sheng Tan ◽  
Linghan Zhang ◽  
Yili Ren ◽  
Zhi Wang ◽  
...  

Author(s):  
Senthil Kumar A. V. ◽  
Rathi M.

Online learning has entirely transformed the way of learning by the students. Online tests and quizzes play an important role in online learning, which provides accurate results to the instructor. But, the learners use different methods to cheat during online exams such as opening a browser to search for the answer or a document in the local drive, etc. They are not authenticated once they login and progress to attend the online exams. Different techniques are used in authenticating the students taking up the online exams such as audio or video surveillance systems, fingerprint, or iris recognition, etc. Keystroke dynamics-based authentication (KDA) method, a behavioral biometric-based authentication model has gained focus in authenticating the users. This chapter proposes the usage of KDA as a solution to user authentication in online exams and presents a detailed review on the processes of KDA, the factors that affect the performance of KDA, their applications in different domains, and a few keystroke dynamics-based datasets to authenticate the users during online exams.


2018 ◽  
Vol 27 (5-6) ◽  
pp. 292-305 ◽  
Author(s):  
Saud Nejr Alotaibi ◽  
Steven Furnell ◽  
Nathan Clarke

2014 ◽  
Vol 989-994 ◽  
pp. 4514-4518
Author(s):  
Yi Fei Yuan ◽  
Lian Zhong Liu ◽  
Chun Yan Han

The BYOD (Bring Your Own Device) scenario puts traditional two-factor authentication into great security challenge. To strengthen the authentication in BYOD scenario, we propose a security compliance based mobile user authentication scheme, which includes a novel calculation method for device identifier, and a fine-grained compliance strategy and its management. The scheme provides a secure authentication mechanism for BYOD scenario and satisfies the requirements of device authentication in mobile user authentication.


2021 ◽  
pp. 63-67
Author(s):  
Karel Charvát ◽  
Michal Kepka

AbstractCrowdsourcing together with Volunteered Geographic Information (VGI) are currently part of  a broader concept – Citizens Science. The methods provide information on existing geospatial data or is a part of data collection from geolocated devices. They enable opening parts of scientific work to the general public. DataBio Crowdsourcing Solution is a combination of the SensLog server platform and HSLayers web and mobile applications. SensLog is a server system for managing sensor data, volunteered geographic information and other geospatial data. Web and mobile applications are used to collect and visualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.


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