DiffLoc+: Toward Robust Wi-Fi Hidden Camera Localization Based on Electromagnetic Diffraction

The proliferation of hidden WiFi cameras has raised serious privacy concerns, making their accurate detection and localization essential for the secure development of future intelligent wireless networks. However, existing solutions often require substantial user involvement, large movement spaces, predefined system parameters, or pre-collected training data, limiting their practicality and scalability. In this paper, we present DiffLoc+, a novel and low-cost system that localizes hidden WiFi cameras by harnessing the fundamental physical principle of electromagnetic diffraction. When an obstacle crosses the line-of-sight path between a transmitter and a receiver, it causes a distinctive signal attenuation pattern. We theoretically analyze the feasibility of exploiting this phenomenon for localization and identify two key conditions for building an unbiased diffraction-based model: symmetry and observability. To satisfy these conditions, DiffLoc+ introduces a controllable diffraction generation mechanism that precisely rotates a small metal plate around a WiFi receiver (e.g. a Raspberry Pi), producing a stable and predictable diffraction 'shadowing' effect. We then construct an unbiased localization model that maps this effect to the azimuth of the camera. To ensure the robustness of the theoretical model in real-world applications, DiffLoc+ further introduces two robustness-enhancing mechanisms: 1) an attenuation-region difference-driven subcarrier selection method, which filters subcarriers that reliably reflect the diffraction attenuation pattern by quantifying the signal contrast between diffraction- and reflection-dominated regions; and 2) an uncertainty evaluation framework that integrates result consistency and diffraction signal quality to eliminate unreliable estimates. Implemented entirely with commodity off-the-shelf (COTS) hardware, DiffLoc+ achieves an average angular error of 11.92° across six diverse indoor environments and eleven commercial camera models, demonstrating its effectiveness and robustness.