Defeating facial recognition
Today's databases are jam-packed with, well, our data, from our license plate numbers to our fingerprints to photos of our actual faces. But that doesn't sit right with some hackers, who take issue with all the ways we can be tracked and surveilled through technology. So the world's best black hats have come up with a clever solution to defeat databases: feed them with faulty data to make the systems less effective and more expensive. Here are some of their most resourceful methods for fooling surveillance systems, and how you can follow suit.
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Subscriber Account active since. You're on camera — or you were at some point in the past few years — and now your face is public domain. Facial recognition technology is everywhere, and only becoming more pervasive. It's marketed as a security feature by companies like Apple and Google to prevent strangers from unlocking your iPhone or front door. It's also used by government agencies like police departments. More than half of adult Americans' faces are logged in police databases, according to a study by Georgetown researchers.
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Facial-recognition tech can see around hoodies or big shades, so pair them with a face covering. Plus, you'll get protection against coronavirus particles and tear gas. There are makeup tutorials online for edgy face paint intended to trick face-recognizing algorithms , but these designs are unproven. Also, it's probably easier for humans to track you if you look like a member of Insane Clown Posse.
A team from the cybersecurity firm McAfee set up the attack against a facial recognition system similar to those currently used at airports for passport verification. By using machine learning, they created an image that looked like one person to the human eye, but was identified as somebody else by the face recognition algorithm—the equivalent of tricking the machine into allowing someone to board a flight despite being on a no-fly list. To misdirect the algorithm, the researchers used an image translation algorithm known as CycleGAN, which excels at morphing photographs from one style into another. For example, it can make a photo of a harbor look as if it were painted by Monet, or make a photo of mountains taken in the summer look like it was taken in the winter. After generating hundreds of images, the CycleGAN eventually created a faked image that looked like person A to the naked eye but fooled the face recognition into thinking it was person B.