This adversarial pattern can prevent surveillance cameras from detecting you

Bill Swearingen has spent the past year running largely the same test, over and over again. The goal was to produce a computer-generated pattern that could block the surveillance cameras lining America’s streets from detecting it. Some 31 million tests later, Swearingen says he can now produce patterns on-demand that, when applied to clothing and objects, prevent some of the most commonly deployed license plate readers and surveillance cameras from detecting whatever the pattern covers, from people to vehicles. His project, which he calls noRecognition, allows people to escape the automatic detection and algorithmic surveillance used across the U.S. and beyond. In recent years, surveillance cameras have been supercharged with the ability to detect what is happening in the footage being recorded, from tracking the license plates of speeding vehicles to using facial recognition to identify suspected criminals, albeit with mixed success and sometimes terrifying results. The detection algorithms that power most surveillance cameras today can sift through vast amounts of footage, allowing law enforcement to pick out activity of interest, akin to pulling a needle out of a haystack. Swearingen’s computer-generated patterns do not block surveillance cameras from recording video footage. Instead, they scramble the camera’s ability to identify objects, people, or faces, so that the cameras do not trigger any detection alerts. By blocking the camera’s ability to detect what the pattern covers, the person becomes a needle in a haystack again — until someone knows where to look. “Privacy is a fundamental right,” Swearingen told TechCrunch in a call this week. He described his patterns as a way to allow people to “opt-out of being tracked.” In its first public test Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen successfully demonstrated the pattern printed on a vehicle, proving that these patterns can be effective at defeating surveillance detection in the real world. Teaching a model how to paint In a call from his home in Kansas City, where he co-founded cybersecurity meet-up SecKC, Swearingen told TechCrunch that as a cyber professional he is acutely aware of the privacy and security risks of surveillance. He described how his town is swamped with surveillance cameras, sometimes located just a few feet from each other. He said that he and others never opted in to being watched, just like he never opted-in to having the government use his driver’s license for facial recognition. 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. Image Credits:Bill Swearingen 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