Researchers demonstrated on Wednesday that they can fool a modern face recognition system into seeing someone who isn’t there.

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.

“If we go in front of a live camera that is using facial recognition to identify and interpret who they’re looking at and compare that to a passport photo, we can realistically and repeatedly cause that kind of targeted misclassification,” said the study’s lead author, Steve Povolny.

How it works

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.

Examples of how cycleGAN morphs photos from one style into another, including turning a photo into a Monet, a horse into a zebra, and a summer landscape into a winter landscape.

The McAfee team used 1,500 photos of each of the project’s two leads and fed the images into a CycleGAN to morph them into one another. At the same time, they used the facial recognition algorithm to check the CycleGAN’s generated images to see who it recognized. 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.

The intermediate stages of CycleGAN morphing person A into person B

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By: Karen Hao
Title: The hack that could make face recognition think someone else is you
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Published Date: Wed, 05 Aug 2020 11:00:00 +0000