WhoFi System Enables Biometric Identification Through Wi-Fi Signal Analysis
Researchers at La Sapienza University developed WhoFi, a deep learning system that identifies and tracks individuals by analyzing how their bodies distort Wi-Fi signals, achieving 95.5% accuracy. The technology raises concerns about covert biometric surveillance since it can track people through walls without their knowledge or consent, using ubiquitous Wi-Fi infrastructure.
What happened
Researchers at La Sapienza University in Rome developed WhoFi, a system using Channel State Information (CSI) from Wi-Fi router signals to identify and track individuals. The system analyzes how a person's body alters Wi-Fi radio signals in unique, recognizable patterns, converting these patterns into vector signatures via deep learning.
In testing on 14 participants across various clothing scenarios, WhoFi achieved 95.5% accuracy in identifying individuals. The system operates through walls and is unaffected by lighting conditions, clothing, or line of sight, relying instead on how radio waves interact with internal body structures.
Reported impact
The technology raises concerns about invisible, unwanted or illegal surveillance. Anyone transmitting Wi-Fi signals could unknowingly share biometric information about nearby individuals, even those not carrying devices.
Potential applications range from benign retail tracking (sending customers promotional messages upon return) to tracking individuals' physical locations and movements across time and space, including in private residences if data were obtained.
Index analysis
WhoFi demonstrates significant capability for covert biometric identification and tracking without visual data capture. The 95.5% accuracy on basic Wi-Fi hardware (TP-Link N750 routers) suggests the technique could be widely deployed using existing infrastructure.
Classification
Sources and evidence
Sources not yet published
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