The Tools to Defeat Facial Recognition Are Free Online

It only takes two stickers to fool this popular face detector

Dave Gershgorn
OneZero
Published in
3 min readOct 21, 2019

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Credit: Klim Kireev/YouTube

FFacial recognition software is far from perfect — research has shown that it’s plagued with racial bias, for example — and now researchers have identified a flaw with the robotic gaze.

Research from Huawei’s Moscow Research Center details one way to thwart a popular open-source algorithm used to detect whether there’s a face in an image or not, a crucial first step before the system matches that face against a database of known faces.

The Huawei paper shows how two stickers, each with a specific pattern that looks like a deformed QR code, can fool a face detection algorithm with 95% accuracy once they’re placed on a subject’s cheeks. If you’re savvy, you can fork the code and play with it yourself on GitHub.

The attack on the face detector turns the algorithm against itself — it only works because the researchers had access to the program they were trying to fool. But that doesn’t mean it’s useless. Many commonly used facial recognition tools are built on open-source software that is available to all.

To develop the sticker hack, the team first took photos of themselves with checkered patches on their cheeks. Then they built an algorithm to alter the checkered cheek patterns in the images, and test whether that changed the confidence of the algorithm that there was a face in the image. The algorithm was set to tinker with the checkered boxes hundreds of times, checking its confidence again and again, until further changes didn’t lower the probability of detecting a face.

When the altered checkered patches were printed and applied to a face in real life, they still evaded the face detector.

Researchers noted that the patterns the algorithm generated were specific to the person in the original image — meaning each evasion has to be tailored to its user.

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Dave Gershgorn
OneZero

Senior Writer at OneZero covering surveillance, facial recognition, DIY tech, and artificial intelligence. Previously: Qz, PopSci, and NYTimes.