Attacking mmWave Imaging With Neural Meta-Material Rendering

Millimeter-wave (mmWave) radar imaging has shown remarkable potential in critical applications. While previous researches have explored attacks on high-level radar perception, the vulnerability of low-level radar imaging to adversarial attacks remains largely unexplored. In this work, we introduce mmHide, the first general attack framework on mmWave radar imaging that utilizes neural rendering of meta-materials to hide imaging targets (e.g., handguns). mmHide’s novelty lies in its three-fold approach: 1) an implicit neural rendering network that efficiently represents and optimizes complex 3D meta-material structures, 2) an explicit differentiable forward imaging model that provides physical constraints, and 3) a self-supervised learning strategy that iteratively refines the meta-material design. This unique combination enables mmHide to create an “invisible cloak” for target objects while maintaining plausible imaging results. Extensive real-world experiments demonstrate mmHide’s effectiveness in significantly reducing target visibility while preserving background similarity. A user study confirms its high success rate in deceiving human observers, outperforming existing methods. These findings not only showcase the potential of our approach but also underscore the urgent need for robust defense mechanisms in mmWave imaging systems.