Near-field millimeter-wave (mmWave) imaging is widely deployed in safety-critical applications such as airport passenger screening, yet its own security remains largely unexplored. This paper presents a systematic study of the adversarial robustness of mmWave imaging algorithms under waveform-domain physical attacks that directly manipulate the image reconstruction process. We propose a practical white-box adversarial model and develop a differential imaging attack framework that leverages the differentiable imaging pipeline to optimize attack waveforms. We also construct a real measured dataset of clean and attack waveforms using a mmWave imaging testbed. Experiments on 10 representative imaging algorithms show that mmWave imaging is highly vulnerable to such attacks, enabling an adversary to conceal or alter targets with moderate transmission power. Surprisingly, deep-learning-based imaging algorithms demonstrate higher robustness than classical algorithms. These findings expose critical security risks and motivate the development of robust and secure mmWave imaging systems.

翻译：近场毫米波成像广泛部署于机场安检等安全关键型应用中，但其自身安全性尚未得到充分研究。本文系统研究了毫米波成像算法在直接操控图像重建过程的波形域物理攻击下的对抗鲁棒性。我们提出了一种实用的白盒对抗攻击模型，并开发了一种基于可微成像流水线的差分成像攻击框架以优化攻击波形。同时利用毫米波成像测试平台构建了包含干净波形与攻击波形的实测数据集。在10种代表性成像算法上的实验表明，毫米波成像对此类攻击高度脆弱，攻击者可通过中等发射功率实现目标隐藏或篡改。令人惊讶的是，基于深度学习的成像算法展现出比经典算法更高的鲁棒性。这些发现揭示了关键安全风险，并推动了鲁棒安全毫米波成像系统的研发。