mmGuard: A Countermeasure Against Physical Adversarial Attacks on mmWave Radar Sensing
Summary
Physical adversarial attacks (PAAs, carefully crafted materials or objects that trick radar systems into giving wrong readings) threaten mmWave radar (millimeter-wave radar, a type of sensor used in autonomous vehicles and security systems) by manipulating its signals, but detecting these attacks has been difficult. mmGuard is a defense framework that identifies adversarial attacks by looking for three telltale physical signatures: spatial phase discontinuities (unnatural patterns in how radar waves reflect), anomalous radar cross-section patterns (unusual reflections), and violations of natural physics-based relationships, achieving over 90% detection accuracy.
Solution / Mitigation
mmGuard addresses the threat through multi-domain feature extraction to capture adversarial signatures, neural refinement to improve detection, and per-object attack detection and mitigation compatible with automotive radar update rates. The paper notes that few-shot adaptation enables calibration to unseen settings.
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11541221
First tracked: July 2, 2026 at 08:03 PM
Classified by LLM (prompt v3) · confidence: 85%