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.

翻译：活体检测已从生物特征认证中抵御呈现攻击和重放攻击的防护措施，演变为现代数字系统中区分人类用户与非人智能体的基本需求。生成式人工智能与智能体人工智能的出现进一步放大了这一需求，将活体检测定位为一项基础安全原语。现有方法面临关键限制，包括依赖显式用户交互、专用硬件、易受日益逼真的欺骗攻击影响，以及在实际部署中可扩展性有限。我们提出A-Live，一种无源活体检测框架，仅利用商用设备中惯性测量单元（IMU）信号即可运行。A-Live基于以下观察：人类运动控制固有的神经肌肉微动会在惯性数据中产生细微但可测量的特征，而这些特征在先前工作中常被视为噪声。我们设计了轻量级特征提取流水线和适合实时设备端部署的紧凑分类器，并引入可控物理微动平台以评估对工程化非人类运动的鲁棒性。在Android和iOS设备上的全面评估（涵盖自动化与真实用户场景）表明，A-Live实现了超过99.5%的准确率，且误接受率与误拒绝率均较低。我们的结果表明，神经肌肉微动特征为新兴AI驱动威胁模型下的活体检测提供了可扩展且无源的基础。