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Dazzle AI with a fashion statement, and it might see nothing

Дата публикации: 15-08-2026 16:13:54



Основное содержимое страницы с новостью.

In a recent article from TechCrunch, Security Editor Zack Whittaker describes an interesting software project being tackled by coder Bill Swearingen. It's one that demonstrates a point I have repeated often: In general - when you use AI to create a solution, you often don't know exactly what that solution is.

Simply put, Bill is developing Dazzle Camouflage patterns to defeat automatic people-recognition systems such as Flock. And, at least at this stage, he appears to be technically very successful. Both Bill and Flock use AI.

Before going further, you should know that dazzle camouflage has been around since zebras began roaming and by people for at least a century. Here's a British warship - probably WW1.

Screenshot 2026-08-11 150947.webp

Automakers currently use it when road testing new designs - like this Jaguar:

Screenshot 2026-08-11 152036.webp

You may be wondering what the point of this "camouflage" is. Certainly, if you saw that first thing floating in the North Atlantic, you would see it as a some kind of ship. And that car is most certainly a car. But it is more difficult to tell precisely which direction that ship is heading or exactly what new style points have been added to the Jaguar.

As it turns out, AI generated algorithms for detecting people and cars are more thoroughly deceived.
Here's one of Bills photo pairs:

pattern.webp

In human terms, it's perhaps a fashion statement. In AI terms, she isn't there.

Another of his photos:

donut-media-car-swearingen.webp

Perhaps '70's? But for AI, it is gone.

Quoting the TechCrunch article, he describes how these patterns were developed:

His proof of concept evolved over time into a reinforcement learning model, essentially a self-contained system that could train itself on which patterns work and which do not against the specific camera algorithms he is testing. In simple terms, Swearingen told TechCrunch that he essentially taught his model “how to paint.”

Each time a pattern failed and an algorithm detected it, the model would try again, over and over, until it eventually defeated multiple algorithms at once.

His model soon began to find perfect recipes for patterns that were able to defeat all of the 11 open source detection algorithms he tested, including the software that powers Flock license plate readers, Axon body-worn cameras, and cameras running Clearview AI.

I do not doubt that systems like Flock will eventually do better. Those systems are currently relying on short-cuts, small "tip offs" that happen to be commonly found with pedestrians, faces, or traffic. As evidenced by Bill's work, they are not looking at the "conclusive" evidence - most certainly because that would require 3d modelling and perhaps tracking objects from frame-to-frame - or with methods based on "people-are" as "people-do".

But don't think about this the next time you want to catch a cat nap in your car while your AI takes over navigation. It would keep you up for the whole trip.

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