Enterprise AI Analysis
WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
This paper introduces WaveVerif, a novel framework for verifying robotic workflows using acoustic side-channel analysis (ASCA). It leverages sounds generated by robot movements to detect discrepancies between intended and actual behavior, enhancing security and operational integrity. Through machine learning models, WaveVerif achieves high accuracy in classifying individual robot movements and complex workflows, even under varying conditions like speed, distance, and microphone placement. This non-invasive, low-cost approach provides a robust solution for real-time verification in sensitive robotic environments without hardware modifications.
Key Executive Impact Metrics
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Accuracy for individual movements
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Accuracy for workflows
Low
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Deep Analysis & Enterprise Applications
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Acoustic Verification Workflow
Robot Movement
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Acoustic Emission Capture
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Feature Extraction
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Machine Learning Classification
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Behavioral Verification
85%
Key Finding: Baseline Accuracy (DNN & CNN)
Impact of Operational Parameters
| Parameter | Impact on Accuracy |
|---|---|
| Movement Distance (D) | Accuracy largely maintained, peaking at 5mm (89-93%), minor drops at 25mm. Robust. |
| Movement Speed (S) | Accuracy decreases by ~10% vs. distance. Upward trend with increasing speed (70-86%). |
| Microphone Distance (M) | Accuracy unexpectedly increases with distance up to 50cm, then slightly drops at 100cm (80-92%). Complex interaction. |
Real-world Application: Warehouse Automation
Scenario: A logistics company deploys uArm Swift Pro robots for pick-and-place and packing tasks. Ensuring robots follow programmed commands precisely is critical to prevent errors, damage, and security breaches.
Challenge: Existing monitoring relies on internal telemetry, which is vulnerable to manipulation. An external, passive verification method is needed.
Solution: WaveVerif is implemented with strategically placed smartphones capturing acoustic emissions. The system's DNN model verifies movements.
Outcome: Achieved 86% accuracy in identifying packing workflows, significantly reducing errors and providing an independent verification layer. Detected minor discrepancies in movement speed early, preventing potential package damage.
Data Preprocessing Steps
Raw Audio Capture
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Bandpass Filtering & Normalisation
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Segmentation (1-second chunks)
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Feature Extraction (RMSE, ZCR, MFCCs)
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Metadata Annotation
86%
Key Finding: Workflow Accuracy (DNN)
Machine Learning Model Performance (Baseline)
| Model | Accuracy | Strengths | Weaknesses |
|---|---|---|---|
| SVM | 83% |
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| DNN | 85% |
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| RNN | 80% |
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| CNN | 85% |
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