A-Live: Passive Liveness Detection via Neuromuscular Micro-Motion Signatures on Commodity Sensors

2026-06-03

AI Summary

This work proposes a passive liveness detection method leveraging inertial measurement unit (IMU) signals from commercial mobile devices, circumventing the need for user interaction or specialized hardware. The approach uniquely exploits intrinsic neuromuscular micro-movements—a biologically inherent human trait—as a discriminative cue against sophisticated spoofing attacks. A lightweight feature extraction pipeline and a compact real-time classifier are developed, and a controllable physical micro-motion platform is constructed to rigorously evaluate robustness against non-human motion. Extensive experiments on both Android and iOS platforms demonstrate that the system achieves over 99.5% accuracy in distinguishing genuine users from automated attacks, while maintaining exceptionally low false acceptance and false rejection rates, thereby enabling scalable and secure deployment.

Abstract

Liveness detection has evolved from a safeguard against presentation and replay attacks in biometric authentication to a broader requirement for distinguishing human users from non-human agents in modern digital systems. The emergence of generative and agentic AI further amplifies this need, positioning liveness as a fundamental security primitive. Existing approaches face key limitations, including reliance on explicit user interaction, specialized hardware, vulnerability to increasingly realistic spoofing, and limited scalability in real-world deployments. We present A-Live, a passive liveness detection framework that operates solely on inertial measurement unit (IMU) signals available in commodity devices. A-Live is based on the observation that neuromuscular micro-motions inherent to human motor control produce subtle but measurable signatures in inertial data, which are often treated as noise in prior work. We design a lightweight feature extraction pipeline and a compact classifier suitable for real-time on-device deployment, and introduce a controllable physical micro-motion platform to evaluate robustness against engineered non-human motion. Extensive evaluation across Android and iOS devices, including both automated and real-user settings, shows that A-Live achieves over 99.5\% accuracy with low false acceptance and rejection rates. Our results demonstrate that neuromuscular micro-motion signatures provide a scalable and passive foundation for liveness detection under emerging AI-driven threat models.

Problem

• liveness detection
• neuromuscular micro-motion
• biometric authentication
• spoofing resistance
• commodity sensors

Innovation

• passive liveness detection
• neuromuscular micro-motion
• inertial measurement unit (IMU)
• commodity sensors
• on-device AI