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

• URL: http://arxiv.org/abs/2606.05126v1
• Date: Wed, 03 Jun 2026 17:30:30 GMT
• Title: A-Live: Passive Liveness Detection via Neuromuscular Micro-Motion Signatures on Commodity Sensors
• Abstract summary: We present A-Live, a passive liveness detection framework that operates solely on inertial measurement unit (IMU) signals available in commodity devices.<n>A-Live achieves over 99.5% accuracy with low false acceptance and rejection rates.<n>Our results demonstrate that neuromuscular micro-motion signatures provide a scalable and passive foundation for liveness detection under emerging AI-driven threat models.
• Score: 0.3568466510804538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction [51.76737991200891]
  Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets.<n>We introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions.<n>We propose PassGen, a generative pipeline that synthesizes realistic handover sequences while ensuring hand-object consistency.
  arXiv  Detail & Related papers  (2026-08-13T09:55:59Z)
• Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network [0.30786914102688595]
  This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm.<n>The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN)<n>The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy.
  arXiv  Detail & Related papers  (2026-07-14T15:34:28Z)
• Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion [14.332919759770645]
  Vision-based gesture recognition has been explored as one method for hands-free teleoperation.<n>We propose a multimodal gesture recognition framework that integrates inertial data from Apple Watches on both wrists with capacitive sensing signals from custom gloves.<n>We show that our framework achieves performance comparable to a state-of-the-art vision-based baseline.
  arXiv  Detail & Related papers  (2026-02-27T05:52:04Z)
• Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence [55.512671026669516]
  We propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring.<n>It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities.<n>We show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding.
  arXiv  Detail & Related papers  (2026-01-21T09:11:26Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
  AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
  arXiv  Detail & Related papers  (2025-10-23T13:02:14Z)
• MOTION: ML-Assisted On-Device Low-Latency Motion Recognition [5.0385144315892925]
  We use WeBe Band, a multi-sensor wearable device equipped with a powerful enough MCU to effectively perform gesture recognition entirely on the device.<n>We found that the neural network provided the best balance between accuracy, latency, and memory use.<n>Our results also demonstrate that reliable real-time gesture recognition can be achieved in WeBe Band, with great potential for real-time medical monitoring solutions.
  arXiv  Detail & Related papers  (2025-10-14T01:15:47Z)
• MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving [85.04826012938642]
  MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
  arXiv  Detail & Related papers  (2025-08-04T03:07:54Z)
• Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals [0.8388591755871735]
  Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks.
  arXiv  Detail & Related papers  (2024-11-25T10:51:40Z)
• MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures [4.92674421365689]
  We propose MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler signatures. It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points. MDPose outperforms state-of-the-art RF-based pose estimation systems.
  arXiv  Detail & Related papers  (2022-01-11T21:46:28Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.