Computer vision is an incredibly complex problem, and it’s only by cognitive shortcuts that even humans can see properly — so it shouldn’t be surprising that computers need to do the same thing. One of the shortcuts these systems take is not assigning every pixel the same importance.

Say there’s a picture of a house with a bit of sky behind it and a little grass in front. A few basic rules make it clear to the computer that this is not a picture “of” the sky or the grass, despite their presence.

So it considers those background and spends more cycles analyzing the shape in the middle. They accomplished it by training an adversary system to create small circles full of features that distract the target system, trying out many configurations of colors, shapes, and sizes and seeing which causes the image recognizer to pay attention.

Specific curves that the AI has learned to watch for, combinations of color that indicate something other than background, and so on. Eventually out comes a psychedelic swirl like those shown here.

Put it next to another object the system knows, like a banana, and it will immediately forget the banana and think the picture is “of” the swirl. The names in the images are different approaches to creating the sticker and merging it with existing imagery. Read more from…

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