Haptic-Based User Authentication for Tele-robotic System

TL;DR

This paper introduces a novel haptic-based authentication method for tele-robotic systems, utilizing deep learning on force feedback data to improve security against spoofing and replay attacks.

Contribution

It presents a transformer-based deep learning approach that extracts behavioral features from haptic signals for secure user authentication in tele-robotics.

Findings

Achieves over 90% accuracy in user identification

Effective in distinguishing user-specific force dynamics

Demonstrates robustness against spoofing and replay attacks

Abstract

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.