发表机构
Rutgers University; UCSD（罗格斯大学; 加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GRAW系统，利用生成对抗模仿学习构造扰动信号，在零知识条件下远程降低Wi-Fi人体活动识别系统性能，保护用户隐私，并在多个数据集和模型上验证了其有效性与可行性。
AI 中文摘要
Wi-Fi设备利用信道状态信息（CSI）识别人类活动的能力日益增强，这引发了隐私方面的担忧。为应对这一威胁，我们提出了GRAW，一个对抗系统，作为隐私捍卫者，通过扰动用户设备用于估计CSI的路由器信号，来降低用户设备上的人体活动识别（HAR）系统的性能。GRAW采用生成对抗模仿学习（GAIL）来构造扰动信号，从而无需任何关于目标HAR系统及其输入的信息（即零知识操作）。我们使用在五个环境中收集的数据集（包括我们自己的数据集）对GRAW进行了针对七个代表性HAR模型的评估。我们观察到，GRAW是唯一能将所有测试的HAR模型性能降至随机选择水平的远程攻击方案。在相同的扰动水平下，GRAW的攻击成功率比对比方法高出高达76.7%，同时保持常规Wi-Fi通信中超过99%的数据包成功率。我们通过软件定义无线电的实时空中实验证明了GRAW的可行性。
英文摘要
The growing capability of Wi-Fi devices to identify human activities using channel state information (CSI) raises privacy concerns. To counter this threat, we propose GRAW, an adversary system, acting as a privacy defender, that degrades the human activity recognition (HAR) system at the user device by perturbing the router's signals that the device uses to estimate CSI. GRAW employs generative adversarial imitation learning (GAIL) to construct perturbation signals, and thereby eliminates the need for any information on the target HAR systems and their inputs (i.e., zero-knowledge operation). We evaluate GRAW against seven representative HAR models, using datasets collected in five environments, including our own dataset. We observe that GRAW is the only remote attack scheme that degrades every tested HAR model to a random-selection level. At the same perturbation level, GRAW achieves an attack success ratio up to 76.7% higher than comparison methods, while maintaining over 99% packet success rate on regular Wi-Fi communication. We demonstrate the feasibility of GRAW through real-time, over-the-air experiments with software-defined radios.
Comments15 pages, 14 figures
