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Radar Data Integrity Verification Using 2D QIM-Based Data Hiding.

Raghu Changalvala1, Brandon Fedoruk1, Hafiz Malik1

  • 1Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.

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|September 30, 2020
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Summary

This study introduces a lightweight data hiding framework using 2D Quantization Index Modulation (2D QIM) to ensure sensor data integrity in vehicles. The method verifies data without impacting sensor fusion quality, enhancing automotive cybersecurity.

Keywords:
2D QIMCANkalman filterradar objectssensor data integritysensor fusionwatermarking

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Area of Science:

  • Cyber-Physical Systems
  • Automotive Cybersecurity
  • Data Integrity Verification

Background:

  • Modern vehicles are complex cyber-physical systems relying on Controller Area Network (CAN) for internal communication.
  • CAN networks lack inherent security features, making them vulnerable to attacks that compromise sensor data integrity.
  • Traditional cryptographic methods for data integrity verification increase computational load, latency, and system cost.

Purpose of the Study:

  • To propose a lightweight alternative for verifying sensor data integrity in vehicle CAN networks.
  • To develop a framework using 2-dimensional Quantization Index Modulation (2D QIM) for data hiding.
  • To analyze the framework's effectiveness in detecting and localizing sensor data tampering.

Main Methods:

  • Implementation of a 2D QIM-based data hiding framework.
  • Performance analysis using a radar sensor data transmission scenario in autonomous vehicles.
  • Evaluation of embedding-induced distortion effects on sensor fusion algorithms.

Main Results:

  • The proposed framework effectively verifies sensor data integrity without compromising sensor fusion data quality.
  • The system demonstrates low overall design complexity.
  • The framework can be applied to network interfaces beyond CAN, offering enhanced data traceability.

Conclusions:

  • The 2D QIM-based data hiding framework provides a viable solution for automotive data integrity verification.
  • This approach enhances the safety and security of advanced driver-assistance systems (ADAS) and automated driving.
  • The framework offers a cost-effective and efficient method for securing in-vehicle data communication.