WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

2025-10-29

AI Summary

This work addresses the challenge of real-time behavioral verification of robot command execution. We propose a non-intrusive monitoring method based on acoustic side-channel analysis (ASCA), the first to leverage robot-motion-induced acoustic emissions for workflow-level behavioral verification without hardware modification—enabling low-cost, passive, real-time monitoring. Our approach jointly models acoustic signals using SVM, DNN, RNN, and CNN, while incorporating physical parameters—including motion velocity, direction, and microphone distance—to enhance robustness. Experimental results demonstrate >80% accuracy in single-action recognition and high-confidence identification of representative workflows (e.g., pick-and-place, packaging). The method establishes a novel paradigm for ensuring trustworthy robot execution in sensitive environments and significantly extends the applicability of side-channel analysis to verification of autonomous systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Motion & Tracking

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this paper, we present a framework that uses acoustic side- channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is consistent with expected commands. The evaluation takes into account movement speed, direction, and microphone distance. The results show that individual robot movements can be validated with over 80% accuracy under baseline conditions using four different classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Additionally, workflows such as pick-and-place and packing could be identified with similarly high confidence. Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments without requiring hardware modifications.

Problem

• Verifying robot command execution using acoustic side-channel analysis
• Monitoring real-time robotic behavior consistency with expected commands
• Providing passive verification without hardware modifications in robotic environments

Innovation

• Acoustic side-channel analysis verifies robotic workflows
• Machine learning classifiers identify robot movements accurately
• Passive acoustic monitoring enables real-time verification