mmGuard: A Countermeasure Against Physical Adversarial Attacks on mmWave Radar Sensing

Summary

This paper presents mmGuard, a physics-based defense against physical adversarial attacks (PAAs) on millimeter-wave radar used in autonomous driving and security checking. It detects attacks by identifying spatial phase discontinuities, anomalous radar cross-section patterns, and violations of physico-kinematic relationships, and the authors report over 90% detection accuracy on mmAD, a dataset of over 110,000 annotated radar frames.

Mitigation

mmGuard performs per-object attack detection and mitigation, compatible with automotive radar update rates, and uses few-shot adaptation to calibrate to unseen settings.

Topics

• Adversarial machine learning

Related items

• InfoQCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action RecognitionSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoDoes Target Alignment Mean Target Recovery? An Evidence-Ladder Study of Adversarial Claims on Contrastive EncodersSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoBRANCH: Bypassing Multi-Scanner AI GuardrailsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoDetecting Adversarial Images through Response Profiles of Vision-Language ModelsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoGraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural NetworksSimilar attack · Arxiv (cs.RO + cs.CV security)