Researchers design patch to make people 'virtually invisible' to AI detectors Credit: Lucas Gallone modified by IDG CommCreative Commons
Business Management

Researchers design patch to make people 'virtually invisible' to AI detectors

Researchers at Katholieke Universiteit Leuven have demonstrated how a small, square, printed patch can be used as “cloaking device” to hide people from AI object detectors.

In recent years, Convolutional Neural Networks (CNNs) have become highly effective at recognising people in images and video feeds. They do this by learning what a human looks like by examining thousands of images of people.

But they can be fooled – the researchers demonstrate in their paper Fooling automated surveillance cameras: adversarial patches to attack person detection – with a 40cm by 40cm ‘adversarial patch’.

In their paper, researchers Simen Thys, Wiebe Van Ranst and Toon Goedeme focus on the popular YOLOv2 object detector. They use a number of tricks to fool the detector.

“We did this by optimising an image to minimise different probabilities related to the appearance of a person in the output of the detector. In our experiments we compared different approaches and found that minimising object loss created the most effective patches,” they write.

They then printed out their optimised patches, and tested them out by filming people holding them. The researchers found the patches worked “quite well” as long as they are positioned correctly.

“From our results we can see that our system is able significantly lower the accuracy of a person detector…In most cases our patch is able to successfully hide the person from the detector. Where this is not the case, the patch is not aligned to the center of the person,” the researchers said.

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