81–90 of 239 posts
Earlier quoted context omitted.
Finally, a real security weakness to cite when making fun of people for their mechanical keyboard. Time to start recording the audio of Zoom calls with some particularly loud typers...
Not according to the article.. Microphones are sensitive enough to mount the attack on quieter keyboards.
The example figure shows a key hit every half second, which suggests a pecking style of typing at around 24 wpm. This way the model gets very clean waveforms. I wonder how their approach would work with average or fast typists. The sound profiles might be much harder to link to characters.
Is it more of a physical fingerprint of each key, such that if you swapped keys/springs the model would need to be updated? So it's produced by manufacturing inconsistencies, the way individual typewriters used to be forensically identified?
Or is more each key being identical, but producing a different resonance pattern within the keyboard/laptop due to the shape of all of the matter surrounding it? If you move the keyboard in the room, do you have to re-train the model?
I also wonder how much it varies depending on how hard you press each key -- not at all or a great deal? And what about by keyboard -- when you compare thin MacBook keys with an external full-height keyboard, is one easier/harder to recognize each key on than the other?
Earlier quoted context omitted.
Not according to the article.. Microphones are sensitive enough to mount the attack on quieter keyboards.
What we clearly need are louder keyboards - which overload the mic so as to render keystrokes indistinguishable.
I'm pretty sure Zoom does this by default as part of its noise cancellation (it's potentially even easier since you can use keydown events to help identify, not just the audio stream).
So as long as basic default noise cancellation is on, that would at least prevent this over regular videoconferencing. And because of this, I'm having a hard time thinking of when else this would be a realistic threat, where the attacker wouldn't already have enough physical access to either install a regular keylogger or else a hidden camera.
I find this really hard to believe. If it were really possible then people could do it with their ears, and they would be doing it and showing off that they can do it. The human ear (and brain) are really, really good at finding patterns and getting signal out of noise.
So they generated training data from one laptop and microphone then generated test data with the exact same laptop and microphone in the same setup, possibly one person pressing the keys too. For the Zoom model they trained a new model with data gathered from Zoom. They call it a practical side channel attack but they didnt do anything to see if this approach could generalize at all
By the way, some (most?) videoconferencing software removes keyboard sounds from the audio, because it's particularly a distracting problem with laptops where the microphone is right next to the keys. I'm pretty sure Zoom does this by default as part of its noise cancellation (it's potentially even easier since you can use keydown events to help identify, not just the audio stream). So as long as basic default noise…
A couple years ago for a weekend project I made a simple "audio-mnist" dataset from handwritten digit audio recordings. I never got past a few days worth of work, but open-sourcing it has been on my mind for a minute. This post kicked me into action. Getting some more data, basic CNN examples, etc. could provide a nice starting point for a lot of research and tools.
There is still separate code I'd have to find and make intelligible to create the recordings and split the audio.
Anyway, in case anyone finds part of this process interesting or useful.
So they generated training data from one laptop and microphone then generated test data with the exact same laptop and microphone in the same setup, possibly one person pressing the keys too. For the Zoom model they trained a new model with data gathered from Zoom. They call it a practical side channel attack but they didnt do anything to see if this approach could generalize at all
Asking for “what signal it is detecting” might be better asked from a “what is the greatest signal bearing information” being used… which would help in averting attacks.
This kind of stuff could be real menacing in all sorts of public places like airports, coffee shops and etc.