Humans Can Be Tracked With Unique 'Fingerprint' Based On How Their Bodies Block Wi-Fi Signals

Researchers from La Sapienza University in Rome have developed 'WhoFi,' a system that uses the way a person's body distorts Wi-Fi signals to re-identify them across different locations -- even if they're not carrying a phone. By training a deep neural network on these subtle signal distortions, the researchers claim WhoFi is able to achieve up to 95.5% accuracy. The Register reports: 'The core insight is that as a Wi-Fi signal propagates through an environment, its waveform is altered by the presence and physical characteristics of objects and people along its path,' the authors state in their paper. 'These alterations, captured in the form of Channel State Information (CSI), contain rich biometric information.' CSI in the context of Wi-Fi devices refers to information about the amplitude and phase of electromagnetic transmissions. These measurements, the researchers say, interact with the human body in a way that results in person-specific distortions. When processed by a deep neural network, the result is a unique data signature.

Researchers proposed a similar technique, dubbed EyeFi, in 2020, and asserted it was accurate about 75 percent of the time. The Rome-based researchers who proposed WhoFi claim their technique makes accurate matches on the public NTU-Fi dataset up to 95.5 percent of the time when the deep neural network uses the transformer encoding architecture. 'The encouraging results achieved confirm the viability of Wi-Fi signals as a robust and privacy-preserving biometric modality, and position this study as a meaningful step forward in the development of signal-based Re-ID systems,' the authors say.

Read more of this story at Slashdot.