from the hush dept.
Kassem Fawaz's brother was on a videoconference with the microphone muted when he noticed that the microphone light was still on—indicating, inexplicably, that his microphone was being accessed.
[...] "It turns out, in the vast majority of cases, when you mute yourself, these apps do not give up access to the microphone," says Fawaz. "And that's a problem. When you're muted, people don't expect these apps to collect data."
[...] They found that all of the apps they tested occasionally gather raw audio data while mute is activated, with one popular app gathering information and delivering data to its server at the same rate regardless of whether the microphone is muted or not.
The researchers then decided to see if they could use data collected on mute from that app to infer the types of activities taking place in the background. Using machine learning algorithms, they trained an activity classifier using audio from YouTube videos representing six common background activities, including cooking and eating, playing music, typing and cleaning. Applying the classifier to the type of telemetry packets the app was sending, the team could identify the background activity with an average of 82% accuracy.
[...] "With a camera, you can turn it off or even put your hand over it, and no matter what you do, no one can see you," says Fawaz. "I don't think that exists for microphones."