Ordinary WiFi Networks Can Track People With Near-Perfect Accuracy Without Cameras

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Researchers at the Karlsruhe Institute of Technology have demonstrated a new surveillance method that uses ordinary, unencrypted Wi-Fi networks to identify and track individuals without cameras or specialized sensors. Led by cybersecurity experts including Professor Thorsten Strufe and Julian Todt at KASTEL, the team showed that standard wireless signals routinely exchanged between local routers and nearby devices can be analyzed to create radio-based images. In tests involving 197 human participants, the machine-learning-driven system successfully recognized specific subjects within seconds with near-perfect accuracy, regardless of their walking posture. The technique exploits unencrypted beamforming feedback information (BFI) sent within local networks. Because the system relies on ambient radio waves generated by active devices in the environment, targets do not need to carry a smartphone or active electronics for identification to occur. The researchers published these findings to warn of growing privacy vulnerabilities across ubiquitous wireless infrastructure.

Prepared by Jonathan Pierce and reviewed by editorial team.

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Articles Published:: 27
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Left: Framing emphasizes corporate and governmental overreach threatening civil privacy rights. Center: Framing focuses objectively on technical mechanisms and academic research findings. Right: Framing highlights regulatory challenges and potential security vulnerabilities for businesses.

Original Source

Karlsruhe Institute of Technology published the research findings on August 12, 2026. Direct URL to the original triggering source, where available but only 1: http://www.sciencedaily.com/releases/2026/08/260811052857.htm

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