> The method takes advantage of normal network communication between connected devices and the router. These devices regularly send feedback signals within the network, known as beamforming feedback information (BFI), which are transmitted without encryption and can be read by anyone within range.
> By collecting this data, images of people can be generated from multiple perspectives, allowing individuals to be identified. Once the machine learning model has been trained, the identification process takes only a few seconds.
> In a study with 197 participants, the team could infer the identity of persons with almost 100% accuracy – independently of the perspective or their gait.
So what's the resolution of these images, and what's visible/invisible to them? Does it pick up your clothes? Your flesh? Or mosty your bones?
Various cheating to get their conclusions (from the paper):
> To allow for an unobstructed gait recording, participants were instructed not to wear any baggy clothes, skirts, dresses or heeled shoes.
> Due to technical unreliabiltities, not all recordings resulted in usable data. For our experiments, we use 170 and 161 participants for CSI and BFI, respectively. [out of 197]
I wish they had explained what the technical unreliabilities were.
Not sure how many people are aware that the newer Alexa devices have "presence detection" that uses ultrasound so they can detect when people are nearby. [0]
Heck, even Ecobee remote temperature sensors can do this.
Reminds me of the story about how the Google Nest smoke detector had a microphone in it. [1]
This is a VERY controlled environment - and they used 20 passes of each person walking with direct knowledge of each person to train for identity. They did no tests with multiple people walking at the same time, or with any other external moving distortion effects (doors opening, etc) . This is very far from actual 'identification' of people in real public settings - and no doubt the cell phone everyone is carrying with them offers many orders of magnitude better opportunity. In a real crowded environment this would be nearly worthless.
The devices that reported BFI information were also stationary, and there were no extra devices transmitting information that would be conflicting.
the article is off-base with wifi. the real story is in 6G cellular.
there is a working group at 3gpp, an EU-funded research group (6th sense, Open6GHub), universities (NCSU, Bristol), and many companies working very hard right now on proposals to include "integrated/joint sensing and communication" (ISAC/JCAS) in the 6G spec.
ISAC means adding mmWave to 6G (ostensibly for speed, but also) to build a high-fidelity 3d realtime "digital twin" of the real world that can see through walls, owned and operated by your telecom provider.
> A very exciting innovation that 6G will bring to the table would be its ability to sense the environment. The ubiquitous network becomes a source of situational awareness, collating signals that are bouncing off objects and determining type and shape, relative location, velocity and perhaps even material properties. With adequate 6G solutions for privacy and trust, such a mode of sensing can help create a “mirror” or digital twin of the physical world in combination with other sensing modalities.
there's been a testbed deployment in a German hospital for "non-invasive" monitoring of vitals; which sounds to me like it can literally see a heartbeat.
truth is, this is the nature of wireless radios. we can't keep improving bandwidth and latency without also turning the radio into a camera. i'm disturbed by the inevitability.
If I was interested in mass surveillance I would combine the radio data (WLAN, BT, ...) with the camera feed. If you then see the same person with ML, you can correlate that with radio's. You can even do that with cell towers with anonymous SIM, especially if combined with public transport camera feed or ALPR/ANPR.
HEADLINE: Electromagnetic radiation can be used to see!!!!
Right, that's what your eyes do. Radio is much longer wavelength than visible light (~5-10cm). So at best it offers extremely crappy resolution unless - you're doing something clever with second order information.
I don't see how this is categorically any different from hidden networked cameras. Perhaps that's the real issue we should be focusing on in terms of privacy and mass surveillance.
I’m not understanding this. You still have to deploy a piece of hardware to read the Wi-Fi waves. Why wouldn’t you just deploy some other piece of hardware that’s better at surveilling the surroundings?
I’m not understanding this. You still have to deploy a piece of hardware to read the Wi-Fi waves. Why wouldn’t you just deploy some other piece of hardware that’s better at surveilling the surroundings?
Also, if the Wi-Fi device is in the area are not busy now your camera is off that doesn’t seem good
I’m not understanding this. You still have to deploy a piece of hardware to read the Wi-Fi waves. Why wouldn’t you just deploy some other piece of hardware that’s better at surveilling the surroundings?
Also, if the Wi-Fi device is in the area are not busy now your camera is off that doesn’t seem good. Also, I imagine you have to tune it for every environment, geometry that doesn’t sound easy.
I’m not understanding this. You still have to deploy a piece of hardware to read the Wi-Fi waves. Why wouldn’t you just deploy some other piece of hardware that’s better at surveilling the surroundings?
Also, if the Wi-Fi device is in the area are not busy now your camera is off that doesn’t seem good. Also, I imagine you have to tune it for every environment, geometry that doesn’t sound easy. And then after all that work, I move my Wi-Fi router 4 inches to the left.
Android devices already know exactly where they are even with GPS disabled, because they sniff the nearby WIFI networks and then ask Google where they are. QED Google knows already, all combined is mass metadata surveillance already provided to those that tap into it.
Any sub-meter precision or presence detection does not really matter, if these companies have all your other questions, queries, messages, calendars, browse history, app usage, and streaming behaviour as well.
You can do it to yourself[1], I am using Tommy for presence detection in Home Assistant, works great (my house is small, so two ESP32s works fine, I am sure having 3-4 would let it see my cat breathing).
78 comments
[ 0.19 ms ] story [ 1592 ms ] thread> By collecting this data, images of people can be generated from multiple perspectives, allowing individuals to be identified. Once the machine learning model has been trained, the identification process takes only a few seconds.
> In a study with 197 participants, the team could infer the identity of persons with almost 100% accuracy – independently of the perspective or their gait.
So what's the resolution of these images, and what's visible/invisible to them? Does it pick up your clothes? Your flesh? Or mosty your bones?
Already is and widely used for exactly what the article worries about
That a super impressive! I wonder how that would be at scale, with a few millions people. I’m don’t think that would remain as accurate
> To allow for an unobstructed gait recording, participants were instructed not to wear any baggy clothes, skirts, dresses or heeled shoes.
> Due to technical unreliabiltities, not all recordings resulted in usable data. For our experiments, we use 170 and 161 participants for CSI and BFI, respectively. [out of 197]
I wish they had explained what the technical unreliabilities were.
Heck, even Ecobee remote temperature sensors can do this.
Reminds me of the story about how the Google Nest smoke detector had a microphone in it. [1]
0 - https://www.amazon.com/b?node=23435461011&tag=googhydr-20&hv...
1- https://www.reddit.com/r/privacy/comments/asmusq/google_says...
The devices that reported BFI information were also stationary, and there were no extra devices transmitting information that would be conflicting.
A single camera would be much more effective.
there is a working group at 3gpp, an EU-funded research group (6th sense, Open6GHub), universities (NCSU, Bristol), and many companies working very hard right now on proposals to include "integrated/joint sensing and communication" (ISAC/JCAS) in the 6G spec.
ISAC means adding mmWave to 6G (ostensibly for speed, but also) to build a high-fidelity 3d realtime "digital twin" of the real world that can see through walls, owned and operated by your telecom provider.
> A very exciting innovation that 6G will bring to the table would be its ability to sense the environment. The ubiquitous network becomes a source of situational awareness, collating signals that are bouncing off objects and determining type and shape, relative location, velocity and perhaps even material properties. With adequate 6G solutions for privacy and trust, such a mode of sensing can help create a “mirror” or digital twin of the physical world in combination with other sensing modalities.
https://www.nokia.com/about-us/newsroom/articles/nokias-visi... https://www.bell-labs.com/institute/blog/building-network-si...
there's been a testbed deployment in a German hospital for "non-invasive" monitoring of vitals; which sounds to me like it can literally see a heartbeat.
https://www.nokia.com/about-us/news/releases/2024/12/17/noki...
truth is, this is the nature of wireless radios. we can't keep improving bandwidth and latency without also turning the radio into a camera. i'm disturbed by the inevitability.
Right, that's what your eyes do. Radio is much longer wavelength than visible light (~5-10cm). So at best it offers extremely crappy resolution unless - you're doing something clever with second order information.
Any sub-meter precision or presence detection does not really matter, if these companies have all your other questions, queries, messages, calendars, browse history, app usage, and streaming behaviour as well.
Even with airplane mode on (that disables GPS, iirc)?
1: https://www.tommysense.com/