On Detecting Hidden Wireless Cameras: A Traffic Pattern-based Approach

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose <monospace>DeWiCam</monospace>, a lightweight and effective detection mechanism using smartphones. The basic idea of <monospace>DeWiCam</monospace> is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, <monospace>DeWiCam</monospace> is more challenging because it cannot access the encrypted information in the data packets. Yet, <monospace>DeWiCam</monospace> overcomes the difficulty and can detect nearby wireless cameras reliably. To further identify whether a camera is in an interested room, we propose a human-assisted identification model. Extension functions of <monospace>DeWiCam</monospace> further enable the video resolution and audio channel inference to provide extra protection. We implemented <monospace>DeWiCam</monospace> on the Android platform and evaluated it with extensive experiments on 20 cameras. The evaluation results show that <monospace>DeWiCam</monospace> can detect cameras with an accuracy of 99 percent within <inline-formula><tex-math notation="LaTeX">$2.7\;\mathrm {s}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>7</mml:mn><mml:mspace width="0.277778em"/><mml:mi mathvariant="normal">s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="cheng-ieq1-2900919.gif"/></alternatives></inline-formula>.