用操作机器人的触觉反馈识别用户，防伪造防重放。
Haptic-Based User Authentication for Tele-robotic System
- 从人机交互的触觉信号中提取用户行为特征
- 用户识别与任务分类准确率超90%
- 适合高安全要求的远程机器人系统
远程操控机器人依赖实时用户行为映射完成远端任务，但安全认证仍面临挑战。传统密码和静态生物特征易受伪造和重放攻击，尤其在高风险、持续交互场景下。本文提出一种新型防伪造、防重放认证方法，利用人机交互过程中独特的触觉反馈行为特征。为评估该方法，我们采集了15名参与者执行7种不同任务的时序力反馈数据集，并构建基于Transformer的深度学习模型，从触觉信号中提取时间特征。通过分析用户特异的力动态，该方法在用户识别与任务分类上均达到90%以上准确率，展现出提升远程机器人系统访问控制与身份认证能力的潜力。
原文摘要 · Abstract (English)
Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To evaluate our authentication approach, we collected a time-series force feedback dataset from 15 participants performing seven distinct tasks. We then developed a transformer-based deep learning model to extract temporal features from the haptic signals. By analyzing user-specific force dynamics, our method achieves over 90 percent accuracy in both user identification and task classification, demonstrating its potential for enhancing access control and identity assurance in tele-robotic systems.
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