压力传感器能偷听说话，黑客可逆向还原语音内容。
A Fly on the Wall -- Exploiting Acoustic Side-Channels in Differential Pressure Sensors
- 利用声音引起的微弱气压变化，从压力传感器读数中提取语音信号。
- 自动识别准确率达90.51%，手动识别错误率仅0.29。
- 适合关注物联网隐私安全的研究者与设备设计者。
差压传感器（DPS）广泛用于监测关键环境，但我们的研究揭示了一种此前未被注意的漏洞：其对气压变化的高度敏感性使其易受声学侧信道攻击。我们证明，传感器膜片会无意中捕捉到语音引发的细微空气振动，这些振动通过传感器结构传播并影响压力读数。基于此发现，我们提出BaroVox攻击方法，可从DPS读数中重建语音，使传感器变为“墙上的苍蝇”。我们建模了声音对DPS的影响，探索了声学泄漏的边界与挑战。为克服这些问题，我们提出两种解决方案：一种基于独特频谱相减的信号处理方法，另一种基于深度学习的关键词分类方法。在多种条件下评估显示，该方法在手动识别中达到0.29的词错误率，自动识别准确率达90.51%。研究揭示了该漏洞的重大隐私风险，并讨论了潜在防御策略。
原文摘要 · Abstract (English)
Differential Pressure Sensors are widely deployed to monitor critical environments. However, our research unveils a previously overlooked vulnerability: their high sensitivity to pressure variations makes them susceptible to acoustic side-channel attacks. We demonstrate that the pressure-sensing diaphragms in DPS can inadvertently capture subtle air vibrations caused by speech, which propagate through the sensor's components and affect the pressure readings. Exploiting this discovery, we introduce BaroVox, a novel attack that reconstructs speech from DPS readings, effectively turning DPS into a "fly on the wall." We model the effect of sound on DPS, exploring the limits and challenges of acoustic leakage. To overcome these challenges, we propose two solutions: a signal-processing approach using a unique spectral subtraction method and a deep learning-based approach for keyword classification. Evaluations under various conditions demonstrate BaroVox's effectiveness, achieving a word error rate of 0.29 for manual recognition and 90.51% accuracy for automatic recognition. Our findings highlight the significant privacy implications of this vulnerability. We also discuss potential defense strategies to mitigate the risks posed by BaroVox.
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