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

2024-05-01

IEEE Transactions on Mobile Computing

AI Summary

To address the vulnerability of behavioral biometrics to spoofing attacks in continuous authentication on mobile devices, this paper proposes FingerSlid—a novel system that actively excites the device using its built-in vibration motor and captures user-specific vibrational responses induced by finger sliding via the accelerometer. Crucially, it extracts physiology-based, behavior-agnostic biometric features rather than action-dependent ones. Methodologically, FingerSlid introduces the first active-vibration-enabled finger-sliding biometric sensing paradigm, establishes a dual-modal signal acquisition pipeline, and designs a Triplet-based deep metric learning network to explicitly suppress motion-related interference—enabling truly behavior-invariant, fine-grained continuous authentication. Experimental evaluation demonstrates an average authentication accuracy of 95.4%, robust resistance to 99.5% of both synthetic and replay attacks, and strong generalizability across diverse real-world scenarios, confirming its practical viability and robustness.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Develops continuous authentication via finger-sliding vibration signals
• Enhances security by eliminating behavioral feature vulnerabilities
• Achieves high accuracy and strong anti-attack resistance

Innovation

• Uses vibration signals for finger-sliding authentication
• Employs Triplet network to remove behavioral influences
• Achieves high accuracy and strong anti-attack performance