Hidden spy cameras have become a great privacy threat recently, as these low-cost, low-power, and small form-factor IoT devices can quietly monitor human activities in the indoor environment without generating any side-channel information. As such, it is difficult to detect and even more challenging to localize them in the rich-scattering indoor environment. To this end, this paper presents the design, implementation, and evaluation of SpyDir, a system that can accurately localize the hidden spy IoT devices by harnessing the electromagnetic emanations automatically and unintentionally emitted from them. Our system design mainly consists of a portable switching antenna array to sniff the spectrum-spread emanations, an emanation enhancement algorithm through non-coherent averaging that can de-correlate the correlated noise effect due to the square-wave emanation structure, and a multipath-resolving algorithm that can exploit the relative channels using a novel optimization-based sparse AoA derivation. Our real-world experimental evaluation across different indoor environments demonstrates an average AoA error of 6.30 deg, whereas the baseline algorithm yields 21.06 deg, achieving over a 3.3 times improvement in accuracy, and a mean localization error of 19.86cm over baseline algorithms of 206.79cm (MUSIC) and 294.75cm (SpotFi), achieving over a 10.41 times and 14.8 times improvement in accuracy.

翻译：近年来，隐蔽式间谍摄像头已成为严重的隐私威胁，这类低成本、低功耗、小尺寸的物联网设备可在室内环境中静默监控人类活动，且不产生任何侧信道信息。因此，检测此类设备已十分困难，在复杂散射的室内环境中对其精确定位则更具挑战性。为此，本文提出SpyDir系统的设计、实现与评估，该系统能够通过捕获间谍物联网设备自动且无意泄漏的电磁辐射，实现对隐蔽间谍设备的精确定位。我们的系统设计主要包括：用于嗅探频谱扩散辐射的便携式切换天线阵列、通过非相干平均实现辐射增强的算法（该算法能消除方波辐射结构导致的噪声相关效应），以及利用相对信道进行多径解析的算法（该算法采用基于优化的新型稀疏到达角推导方法）。我们在不同室内环境下的真实场景实验评估表明，系统平均到达角误差为6.30度，而基线算法误差为21.06度，精度提升超过3.3倍；平均定位误差为19.86厘米，相较于MUSIC算法（206.79厘米）与SpotFi算法（294.75厘米）的基线结果，精度分别提升超过10.41倍与14.8倍。