The technology to convert wifi signals into the placement and identity of people is getting much better. Not by using their devices, just the waves bouncing of their bodies. (There’s nothing new to the pipeline as far as I can tell, we’re just starting to get into the accuracy ranges that make it easy/useful.)
No. You and your neighbors couldn’t unless they had enough access to train up on and analyze how the wifi signals were being disrupted by the people in question. At best you and your neighbors could know when it detected that humans were present and that’s about it.
Unless your neighbors have active access to your home and can collect other data about your walking and moving patterns to fine tune a model.
I’m so tired of this clickbait story. Can we not spread this nonsense.
The original title was substantially less clickbait; I amped it up in the hopes of a reply like yours.
If you have a moment more to enlighten me, I think you’re claiming:
- This technique is location and person specific; the models don’t generalize if you move the router or change the people (and retrain on less data)?
- This method doesn’t determine where folks are (even when used with multiple routers) to any useful precision? (Forget who, you can guess whose in your neighbors house)
- This method is nowhere near getting plausible pose data?
My thought process is this. Your neighbor likely has neither the inclination or the technical knowledge to use disturbances in the wifi signals from their own wifi to do this. Additionally they likely don’t have access to every neighbors wifi (which if you had it would likely allow you to fine tune this significantly).
So basically, every neighbor you have or at least the majority of them are getting together to do this?
The premise is that a person can set up a wifi network in a place adjacent to [target], monitor that network, map the area they want to surveil (while it’s empty would be better) and then use disturbances in that area to track [target] within the space.
What you’d still need is time to measure the disturbances and gather a profile on the individual you were tracking in order to remove false positives (people of the same height and weight or body type etc).
It actually is at least location specific. Because you need penetration of the Wi-Fi signal into the space in order to collect the data necessary. I live in a place that was built on a steel frame with sheet rock. I can’t even get signal from my own wifi standing on my door step. I also don’t pick up the wifi of my neighbors, and can’t get cell signal in the house. So they’d have to break into my home and place monitors to pick up my own wifi in order to do this. At that point it’d be much easier to plant a bug.
Because it’s location specific and dependent on the materials of the construction of the place being penetrable enough to collect said frequencies (and because wifi is often high frequency and the higher the frequency the less penetrative power it has), it’s much more likely that this kind of surveillance method would likely be used by a 3-letter agency, and at that point they’d drop a wifi pineapple on the roof, assuming they needed to do that at all. Or track you through public wifi.
There’s lots of news stories about new and old ways of surveillance and some of those are just easier, less intrusive, less expensive, and possibly more effective.
Some plants have leaves so sensitive you can measure their movements to reconstruct conversations that happened in their vicinity.
I think a few years ago there was a story about listening in on a conversation by during a laser at a window.
You could potentially do the same thing with sonar.
I have questions about why your neighbors would do that. The usefulness of it for your everyday civilian isn’t worth the work, and even if it was it relies on the cooperation of more than one person, and the right location factors to do it.
I also have questions about what happens when there are no users interacting with the wifi.
“This technology turns every router into a potential means for surveillance,” warns Julian Todt from KASTEL. “If you regularly pass by a café that operates a WiFi network, you could be identified there without noticing it and be recognized later – for example by public authorities or companies.”
It is sufficient for Wi-Fi devices in their vicinity to communicate with each other. This creates an image - comparable to a camera image, but based on radio waves.
It exploits the legitimate users who are connected to the Wi-Fi. They regularly send feedback signals, also known as beamforming feedback information (BFI), to the router in the network - unencrypted and readable for third parties. This creates images from different angles that can be used to identify people. This only takes a few seconds once the machine learning model behind it has been trained.
This paragraph in particular suggests that you still need training data. That seems like it would require a larger window of data collection and training.
In the home there must be stationary or rarely moved devices (usually one to three) connected to this router via Wi-Fi — for example, a printer, a smart speaker and/or a smart TV. Sometimes Wi-Fi extenders and mesh Wi-Fi devices can perform the role of a “sensor”.
Motion detection will occur only in the oval zone between the router and the “sensor”, and post-setup testing is required.
https://www.kaspersky.com/blog/wifi-sensing-motion-detection-howto/53851/
https://www.informatik.kit.edu/english/11147_14950.php
https://www.sciencedaily.com/releases/2026/05/260522023127.htm
Thank you. I could be persuaded to change the title, though I don’t think I have been. (You are perhaps not trying to, but I’ll record my reasoning anyway.)
Re: nobody is motivated to do this. True, and doesn’t contradict the title or content. Anybody could drive out and start toppling power poles and poisoning the water supply, fortunately most people are mostly good.
Re: some houses are well insulated. Congrats on your nice home! Many dream of having their own space someday. I think the most interesting case is in an apartment/condo/high density complex. In this setting, you have:
- the layout of the entire building, including other rooms. (modulo furniture and dustables; the walls are public.)
- many routers all over the building, overlapping and generally at different frequencies. When I AirBnB, I often see dozens of different networks from my bedroom. Note that for this style of attack, you don’t need to connect to anything.
- thin walls between units (often cheap).
- some incentive towards snooping. Who stole your packages? Which neighbor keeps letting the dog poop at your window?
Re: training required and the field of view This I find most compelling. I am interested in how much legitimate use is required; can we simply make login attempts? Or does it take somebody logged in? It’s hard for me to tell how customized the model must be (this is a setting where data is reasonably easy to generate in a lot of settings; perhaps enough so that, given a model slightly larger, we get something general?).
Yeah, I’m more concerned about the invasion from the “smart glasses” fad filming fucking everything than I am this pie in the sky prediction.
Install nearby glasses app from fdroid. Might help you notice them if needed.
In public spaces, this is already happening with CTV from nearby stores and such no?
Two wrongs don’t make a right.
“identify people walking within a network’s range”
So it’s dependent on me walking? Good luck! 🤣
the wonder is that folks walked in different ways, and were still identified correctly. Not walking, you might be mistaken for furniture tho.
