Scientists were able to recognize people with almost perfect accuracy via regular Wi-Fi

German scientists from the Karlsruhe Institute of Technology (KIT) have developed a method for identifying people with almost 100 percent accuracy using conventional Wi-Fi signals. This was reported by the journal ScienceDaily on May 22.

Researchers have found that analyzing the reflections of radio waves allows you to create an "image" of the environment and the people in it. At the same time, it is not necessary to have an active device with you for recognition — a switched-off phone also does not guarantee protection.

"By observing the propagation of radio waves, we can create an image of the environment and the people present. This works similarly to a conventional camera, with the difference that radio waves are used instead of light waves. Therefore, it doesn't matter whether you carry a Wi-Fi device with you or not," explained Professor Torsten Strufe from KIT.

The technology uses standard communication protocols between routers and connected devices. Beamforming feedback data is transmitted unencrypted, so anyone in the network's coverage area can potentially intercept it. After training the model, identification takes only a few seconds.

Scientists are particularly concerned that technology is turning every router into a potential surveillance tool.

"If you regularly pass by a cafe with a Wi-Fi network, you may be identified there without being noticed," warned Julian Todt from KIT.

The researchers urge to include more reliable privacy protection and security guarantees in the developed IEEE 802.11bf Wi-Fi standard. The results of the work will be presented at the ACM CCS conference in Taipei.

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Science Daily magazine reported on May 19 that American scientists have developed an experimental diagnostic tool in the form of a computer game that can quickly identify patients with depression based on anhedonia, a key sign of the disorder.

According to the report, the game reliably identifies patients with BDD in just three minutes. It is based on neurobiological data and can be run remotely on a smartphone, while its accuracy is comparable to traditional clinical tests.