Touch-based authentication is widely deployed on mobile devices due to its convenience and seamless user experience. However, existing systems largely model touch interaction as a purely behavioral signal, overlooking its intrinsic multidimensional nature and limiting robustness against sophisticated adversarial behaviors and real-world variations. In this work, we present BioMoTouch, a multi-modal touch authentication framework on mobile devices grounded in a key empirical finding: during touch interaction, inertial sensors capture user-specific behavioral dynamics, while capacitive screens simultaneously capture physiological characteristics related to finger morphology and skeletal structure. Building upon this insight, BioMoTouch jointly models physiological contact structures and behavioral motion dynamics by integrating capacitive touchscreen signals with inertial measurements. Rather than combining independent decisions, the framework explicitly learns their coordinated interaction to form a unified representation of touch behavior. BioMoTouch operates implicitly during natural user interactions and requires no additional hardware, enabling practical deployment on commodity mobile devices. We evaluate BioMoTouch with 38 participants under realistic usage conditions. Experimental results show that BioMoTouch achieves a balanced accuracy of 99.71% and an equal error rate of 0.27%. Moreover, it maintains false acceptance rates below 0.90% under artificial replication, mimicry, and puppet attack scenarios, demonstrating strong robustness against partial-factor manipulation.

翻译：触控认证因其便捷性和无缝用户体验被广泛部署于移动设备。然而，现有系统多将触控交互建模为纯行为信号，忽视了其固有的多维特性，限制了其对复杂对抗行为及现实环境变化的鲁棒性。本文提出BioMoTouch——一种基于关键实证发现的移动设备多模态触控认证框架：在触控交互过程中，惯性传感器捕捉用户特有的行为动态，而电容屏幕同时捕获与手指形态和骨骼结构相关的生理特征。基于此发现，BioMoTouch通过整合电容触控信号与惯性测量，联合建模生理接触结构与行为运动动态。该框架并非组合独立决策，而是显式学习两者的协调交互，形成触控行为的统一表征。BioMoTouch在自然用户交互过程中隐式运行，无需额外硬件，可在商用移动设备上实际部署。我们招募38名参与者在真实使用场景下进行评估。实验结果表明，BioMoTouch的平衡精度达99.71%，等错误率为0.27%。此外，在人工复制、模仿及傀儡攻击场景下，其误接受率始终低于0.90%，展现出对部分因素操控的强鲁棒性。