FingerSlid: Towards Finger-Sliding Continuous Authentication on Smart Devices Via Vibration

• URL: http://arxiv.org/abs/2504.00436v1
• Date: Tue, 01 Apr 2025 05:38:58 GMT
• Title: FingerSlid: Towards Finger-Sliding Continuous Authentication on Smart Devices Via Vibration
• Authors: Yadong Xie, Fan Li, Yu Wang,
• Abstract summary: We propose an authentication system, FingerSlid, which uses vibration motors and accelerometers in mobile devices to sense biometric features of sliding fingers.<n>FingerSlid is evaluated through a large number of experiments under different scenarios.<n>It achieves an average accuracy of 95.4% and can resist 99.5% of attacks.
• Score: 9.39889406833753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, mobile smart devices are widely used in daily life. It is increasingly important to prevent malicious users from accessing private data, thus a secure and convenient authentication method is urgently needed. Compared with common one-off authentication (e.g., password, face recognition, and fingerprint), continuous authentication can provide constant privacy protection. However, most studies are based on behavioral features and vulnerable to spoofing attacks. To solve this problem, we study the unique influence of sliding fingers on active vibration signals, and further propose an authentication system, FingerSlid, which uses vibration motors and accelerometers in mobile devices to sense biometric features of sliding fingers to achieve behavior-independent continuous authentication. First, we design two kinds of active vibration signals and propose a novel signal generation mechanism to improve the anti-attack ability of FingerSlid. Then, we extract different biometric features from the received two kinds of signals, and eliminate the influence of behavioral features in biometric features using a carefully designed Triplet network. Last, user authentication is performed by using the generated behavior-independent biometric features. FingerSlid is evaluated through a large number of experiments under different scenarios, and it achieves an average accuracy of 95.4% and can resist 99.5% of attacks.

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