When your robot vacuum cleaner does its work around the house, beware that it could pick
up private conversations along with the dust and dirt.
Computer scientists from National University of Singapore (NUS) have demonstrated that
it is indeed possible to spy on private conversations using a common robot vacuum cleaner and its
built-in Light Detection and Ranging (Lidar) sensor.
The novel method, called LidarPhone, repurposes the Lidar sensor that a robot vacuum cleaner
normally uses for navigating around a home into a laser-based microphone to eavesdrop
on private conversations.
The proliferation of smart devices – including smart speakers and smart security cameras
– has increased the avenues for hackers to snoop on our private moments.
The NUS researchers' method shows it is now possible to gather sensitive data just by
using something as harmless as a household robot vacuum cleaner.
Their work demonstrates the urgent need to find practical solutions to prevent such malicious
attacks.
The core of the LidarPhone attack method is the Lidar sensor, a device which fires out
an invisible scanning laser, and creates a map of its surroundings.
By reflecting lasers off common objects such as a dustbin or a takeaway bag located near
a person’s computer speaker or television soundbar, the attacker could obtain information
about the original sound that made the objects’ surfaces vibrate.
Using applied signal processing and deep learning algorithms, speech could be recovered from
the audio data, and sensitive information could potentially be obtained.
In their experiments, the researchers used a common robot vacuum cleaner with two sources
of sound.
One was the voice of a person reading out numbers played from a computer speaker, while
the other source was music clips from television shows played through a television soundbar.
The team collected more than 19 hours of recorded audio files and passed them through deep learning
algorithms that were trained to either match human voices or identify musical sequences.
The system was able to detect the digits being spoken aloud, which could constitute a victim’s
credit card or bank account numbers.
Music clips from television shows could potentially disclose the victim’s viewing preferences
or political orientation.
The system achieved a classification accuracy rate of 91 per cent when recovering spoken
digits, and a 90 per cent accuracy rate when classifying music clips.
These results are significantly higher than a random guess of 10 per cent.
The researchers also experimented with common household materials to test how well they
reflected the Lidar laser beam and found that the accuracy of audio recovery varied between
different materials.
They discovered the best material for reflecting the laser beam was a glossy polypropylene
bag, while the worst was glossy cardboard.
To prevent Lidars from being misused, the researchers recommend users to consider not
connecting their robot vacuum cleaners to the Internet.
The team also recommends that Lidar sensor manufacturers incorporate a mechanism that
cannot be overridden, to prevent the internal laser from firing when the Lidar is not rotating.
The team is working on applying ideas learnt from LidarPhone to autonomous vehicles – which
also use Lidar sensors – as they could also be used to eavesdrop on conversations happening
in nearby cars through minute vibrations of the car windows.
They are also looking at the vulnerability of active laser sensors found on the latest
smartphones, which could reveal further privacy issues.