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<sc>MagGuard</sc>: Detecting Mobile Eavesdropping via Built-In Magnetometers With Contrastive Learning
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PRE
AI
H
杨
Y
陈
薛
J
DOI：10.1109/tmc.2026.3697471.png)
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Abstract
En 中文
Protecting privacy-sensitive hardware usage on mobile devices is crucial. Although mobile operating systems (OSs) and smartphone manufacturers have set the permission settings, attackers can evade these defenses using covert methods, enabling malicious camera recording, microphone eavesdropping, and screen capture. Electronic devices emit unique yet weak electromagnetic interference (EMI) signals when accessing privacy-sensitive hardware. But, these signals are easily affected by foreground application activities and geomagnetic fluctuations caused by device movement. Our prior work showed that supervised learning can extract EMI features correlated with privacy hardware states from complex magnetometer readings, but it requires substantial labeled data, limiting practical deployment to new device models or OS versions. To eliminate this reliance on labeled data, this paper proposes a multimodal contrastive learning framework that leverages the device’s built-in magnetometer and synchronized system logs as dual-modal inputs. Through self-supervised training, the framework can learn the intrinsic associations between EMI features and the operating states of privacy-sensitive hardware. Building on this, we design an EMI-based eavesdropping classifier that can analyze a user device’s magnetometer readings offline to detect covert eavesdropping activities. Experimental results show that the proposed method can effectively identify eavesdropping behavior related to access to camera, microphone, and screen recording data. Testing across ten diverse mobile devices achieved an average classification accuracy of 89.1% on Android devices and 88.5% on iOS devices for identifying the specific hardware being eavesdropped upon.
Keywords:
Eavesdropping detection
mobile security
electromagnetic side channel
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