Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition
Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition
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DOI：
10.1109/infocom53939.2023.10228887
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen
中科院分区：
文献类型：
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作者：
Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen
近年来，基于毫米波（mmWave）技术的人类活动识别（HAR）系统由于其更好的隐私保护和增强的传感器分辨率而得到了发展。随着HAR系统部署规模的不断扩大，此类系统的脆弱性也逐渐暴露出来。然而，HAR对抗性攻击的现有努力仅集中在非目标攻击上。在本文中，我们提出了第一个有针对性的对抗攻击毫米波为基础的HAR通过设计的通用扰动。开发了一种实用的迭代算法来制作扰动，该扰动在不同的活动样本之间很好地泛化，而无需额外的训练开销。与现有的仅针对特定的基于毫米波的HAR模型开发对抗性攻击的工作不同，我们通过将我们的目标扩展到两个最常见的基于毫米波的HAR模型（即，基于体素和基于热图）。此外，我们考虑了一个更具挑战性的黑盒场景，通过知识蒸馏解决信息不足问题，并通过生成对抗网络解决活动样本不足的问题。我们评估了两种不同的毫米波为基础的HAR模型设计的健身跟踪提出的攻击。评估结果证明了所提出的有针对性的攻击的有效性，效率和实用性，平均成功率超过90%。
Human activity recognition (HAR) systems based on millimeter wave (mmWave) technology have evolved in recent years due to their better privacy protection and enhanced sensor resolution. With the ever-growing HAR system deployment, the vulnerability of such systems has been revealed. However, existing efforts in HAR adversarial attacks only focus on untargeted attacks. In this paper, we propose the first targeted adversarial attacks against mmWave-based HAR through designed universal perturbation. A practical iteration algorithm is developed to craft perturbations that generalize well across different activity samples without additional training overhead. Different from existing work that only develops adversarial attacks for a particular mmWave-based HAR model, we improve the practicability of our attacks by broadening our target to the two most common mmWave-based HAR models (i.e., voxel-based and heatmap-based). In addition, we consider a more challenging black-box scenario by addressing the information deficiency issue with knowledge distillation and solving the insufficient activity sample with a generative adversarial network. We evaluate the proposed attacks on two different mmWave-based HAR models designed for fitness tracking. The evaluation results demonstrate the efficacy, efficiency, and practicality of the proposed targeted attacks with an average success rate of over 90%.