A-Live: Passive Liveness Detection via Neuromuscular Micro-Motion Signatures on Commodity Sensors
2026-06-03 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors present A-Live, a method to tell if a device user is a real human or a fake agent by using tiny natural movements in the body detected through common sensors in phones. Unlike other methods needing special hardware or user actions, A-Live passively uses subtle motion patterns from muscles that usually get ignored as noise. They built a simple system that runs quickly on devices and tested it on many phones with real and simulated users, achieving over 99.5% accuracy. Their work shows that these small human motion signals can help improve security against advanced fake user attacks.
Liveness detectionBiometric authenticationInertial Measurement Unit (IMU)Neuromuscular micro-motionsSpoofing attacksReal-time classificationOn-device deploymentHuman motor controlAI-driven threatsPassive detection
Authors
Mohammed Gharib, Sam Burns, Martin Zizi
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.