This ‘adversarial’ pattern can prevent surveillance cameras from detecting you

Swearingen described himself as a middle-aged white guy who lives in the center of the United States, and acknowledged that as a result he has not faced hardship or discrimination for being who he is or what he looks like. Swearingen recounted how last year he wanted to attend a protest, but felt uncomfortable and concerned that the vast number of cameras could track people who were exercising their constitutional rights to free expression.

If he felt this way, undoubtedly others would as well, including those who wanted to exercise their rights but may not feel safe or comfortable doing so themselves. Swearingen got to work.

For as long as there have been cameras capable of detecting things, there have been efforts to counter the technology. Several art projects and clothing brands have introduced apparel that aims to help people defeat facial recognition. Some eyeglass makers are jumping on the trend, albeit not with much efficacy. 

Swearingen said his research builds on some of this earlier work, which showed that it was possible to block camera detections. 

He started out last year with a proof-of-concept test lab that began by incrementally defeating one open-source video camera detection algorithm after another. Over the course of the year, he refined the patterns by scaling up his tests with additional computer processing power. He thanked the wider community who showed up with hardware to help further the project along. 

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.

Now the model creates new patterns every minute, each batch mathematically better than the last, he said.

On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen ran his first real-world test. With help from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen’s newest patterns to see if the car would be invisible to detection by a Flock camera.

“We proved it was effective;” said Swearingen; though, the wheels were a challenge, he said. The video of the demo will be out in the next few weeks, said Donut Media.

With a public demo in Las Vegas now under his belt, the project is early proof that it is possible to avoid algorithmic detection in public spaces. The next step is getting the patterns into the hands of people who want them, he said.

The noRecognition project also has a crowdsourcing campaign to help fund the sale of early merchandise featuring the patterns, from T-shirts to hoodies, with the potential for pattern-printed skins for vehicles down the line. Swearingen said the aim is for the patterns to be high quality and resolution good enough to work from a distance, while also looking aesthetically fashionable.

He said he is keeping his strongest patterns off the internet to prevent the camera makers from defeating them, but that the work is not yet done. His models are continuing to grind out new patterns.

“Every failure improves my model, and so keep getting better and better,” he said.