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When ordinary Wi-Fi signals encounter an object in their path, they penetrate and bounce off in ways specific to that object, based on its shape, orientation, and material. This data, the channel state information, can be analyzed to identify the object with high accuracy. Security researchers previously disclosed a technique that lets ordinary Wi-Fi routers identify people with 99.5% accuracy .
To build the WiFireNet system, the researchers first created a dataset, dubbed the WiFire Dataset, by simultaneously recording RGB footage and the reflected Wi-Fi signals of people walking around with objects ranging from phones to handguns. The researchers then trained a deep learning model on the massive dataset, using video clips to match the visuals of a concealed handgun with its specific Wi-Fi signature, or channel state information. To ensure the system could specifically distinguish a handgun from everyday items of similar shape or material, the researchers trained it on recordings of a plastic handgun, a metal water bottle, a real metal handgun, and a phone.
After training, the final AI system, WiFireNet, could detect concealed handguns solely based on Wi-Fi CSI, with varying accuracy. In controlled trials tracking up to four moving individuals in a small indoor space, the system achieved an average handgun-detection precision of 72.5%. However, under stricter localization requirements, such as pinpointing the weapon’s exact location on the carrier, precision dropped to 53.8%. The neural network also struggles to perform accurately in unfamiliar, untested indoor layouts.
Comcast Wi-Fi Shield leverages your router for motion detection
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Reference reading
- https://www.tomshardware.com/tech-industry/artificial-intelligence/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/artificial-intelligence/researchers-train-ai-to-detect-concealed-handguns-using-wi-fi-signals-open-source-wifirenet-achieves-precision-detection-on-older-802-11a-standard-by-analyzing-how-weapons-distort-wireless-signals#main
- https://www.tomshardware.com/membership
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