The Surveillance Detection Scout prototype, whose software Kain has made available on Github, works by capturing and analyzing the video from a Tesla's three cameras—two on its sideview mirrors and one forward-facing—on a $700 Nvidia Jetson Xavier mini-computer. It uses an open source neural network framework called Darknet as its machine learning engine, along with ALPR Unconstrained for recognizing license plates and Facenet for tracking faces. Both of those programs are available for free on Github. The system also uses Google's Open Images Dataset as training data.

"I’m not doing any cutting-edge AI," Kain says. "I’m just applying what’s already freely available, off the shelf." The software even identifies the make and model of cars it sees based on license plate lookups on the service FindByPlate.com. (Kain says it's far harder to link license plates to actual names, and he doesn't intend to include that data in his tool.)

An example of a push notification sent to a phone from Surveillance Detection Scout. Courtesy Truman Kain

Kain says he came up with the idea for his follower detection mechanism last year after he attended a talk on countersurveillance at last year's Defcon. He'd been thinking since he first bought his Tesla Model 3 about the gigabytes of video it collected and deleted, overwriting its video logs every hour. "I had a little bit of FOMO, thinking about how all this video is gone if I don't do something with it," Kain says.

A screenshot of Surveillance Detection Scout’s interface, showing recently detected license plates. Courtesy Truman Kain

After learning about a tool available on Github called Tesla USB that allows Tesla owners to store their video to an external drive indefinitely, Kain came up with the idea of combining that storage capability with image recognition to give his car features similar to the Nest camera in his home, which includes so-called "familiar face detection." Beyond tracking license plates, the face detection element of his tool also functions as what he describes as an upgrade to Tesla's existing Sentry security system, which starts recording when someone touches your car and sets off an alarm if they attempt to break into it.

By stitching together a patchwork of public code, Kain's 4-inch-cubed box can recognize license plate numbers and faces from the car's video stream and alert the car's owner if it spots repeated plates or faces in that data. It uses the software integration tool If This Then That to send alerts. By default, the system will notify the driver if it sees the same car following for every minute over a five-minute span, though Kain says the settings can be adjusted to the driver's preference. The notifications have about a one-minute delay, Kain says, because of the time a Tesla's cameras take to record a video file. And for now, users have to set up their own web server for it to work, though Kain says he may offer simpler web-based logins on his own server in the future.

'A Surveillance Camera on Wheels'

Kain proposes some scenarios where his system could do some good: confidential sources meeting with a journalist, or anyone else who has reason to believe they're being followed or targeted by snoops. "If it helps keeps someone safe, that’s great," Kain says. "If it lets me know that someone’s sneaking around my car, that’s also great."

The Surveillance Detection Scout, however, faces not just ethical issues but also legal ones, says Joseph Lorenzo Hall, the chief technologist with the Center for Democracy and Technology. State laws against automatic license place readers, even for private use, would likely make it illegal in Arkansas, Georgia, Maine, and New Hampshire. Its facial recognition features make it illegal in Illinois.