This website uses cookies
We use cookies to personalise content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners who may combine it with other information that you’ve provided to them or that they’ve collected from your use of their services.
Consent Selection
Details
  • Necessary cookies help make a website usable by enabling basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies.
  • Preference cookies enable a website to remember information that changes the way the website behaves or looks, like your preferred language or the region that you are in.
    • We do not use cookies of this type.

  • Statistic cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously.
    • We do not use cookies of this type.

  • Marketing cookies are used to track visitors across websites. The intention is to display ads that are relevant and engaging for the individual user and thereby more valuable for publishers and third party advertisers.
    • We do not use cookies of this type.

  • Unclassified cookies are cookies that we are in the process of classifying, together with the providers of individual cookies.
    • __emg_sidPending
      Maximum Storage Duration: 1 dayType: HTTP Cookie
      __emg_vidPending
      Maximum Storage Duration: 1 yearType: HTTP Cookie
      nl-read-countPending
      Maximum Storage Duration: PersistentType: HTML Local Storage
Cookie declaration last updated on 8/12/26 by Cookiebot
[#IABV2_TITLE#]
[#IABV2_BODY_INTRO#]
[#IABV2_BODY_LEGITIMATE_INTEREST_INTRO#]
[#IABV2_BODY_PREFERENCE_INTRO#]
[#IABV2_BODY_PURPOSES_INTRO#]
[#IABV2_BODY_PURPOSES#]
[#IABV2_BODY_FEATURES_INTRO#]
[#IABV2_BODY_FEATURES#]
[#IABV2_BODY_PARTNERS_INTRO#]
[#IABV2_BODY_PARTNERS#]
About
Cookies are small text files that can be used by websites to make a user's experience more efficient.

The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies we need your permission.

This site uses different types of cookies. Some cookies are placed by third party services that appear on our pages.

You can at any time change or withdraw your consent from the Cookie Declaration on our website.

Learn more about who we are, how you can contact us and how we process personal data in our Privacy Policy.

Please state your consent ID and date when you contact us regarding your consent.
NewsLayer

Install NewsLayer

Get the app experience — one tap from your home screen, instant loads and breaking-news alerts.

NewsLayer.com
External ReportingPublicado há 6 horas

The AI-Generated Pattern Hides You From Surveillance Cameras—Including Flock

A Kansas City security researcher ran 31 million tests to teach a model how to paint camouflage for the algorithm age.

The AI-Generated Pattern Hides You From Surveillance Cameras—Including Flock
Por Jose Antonio Lanz 3 min de leitura
NewsLayer editorial artwork

Layer Index

↑ 3 pts in 24h

In brief

  • Bill Swearingen’s noRecognition project generates patterns that stop camera software from classifying what it covers—people, faces, or cars.
  • The patterns defeated all 11 open-source detection algorithms he tested, including the software behind Flock license plate readers, Axon body cameras, and Clearview AI.
  • The first public test came Friday at Def Con in Las Vegas: a 2009 Toyota Yaris wrapped in the pattern, driven past a Flock camera.

Bill Swearingen spent the past year running one experiment over and over from his home in Kansas City, where he co-founded the SecKC security meetup. About 31 million tests later, he says he can produce patterns on demand that hide whatever they cover from the detection software wired into Flock cameras—the controversial surveillance system being rolled out across America.

He showed it in public for the first time Friday at Def Con, working with the YouTube channel Donut Media to cover a 2009 Toyota Yaris in one of his newest patterns and roll it past a Flock camera.

Myriad: How many days will Claude go down in August? Click to make your prediction.

“We proved it was effective,” Swearingen told TechCrunch, though he said the wheels were a challenge. Donut Media said video of the demo lands in the next few weeks.

The pattern doesn’t blind the camera. Footage still records normally, and a human watching the screen sees a car. What breaks is the layer on top—the object-detection model that decides “that’s a vehicle, that’s a plate, log it.”

So basically, feed an AI detector with enough visual noise engineered against its own math and it logs nothing. The car goes back to being a needle in a haystack.

Image credit: Donut Media

That’s adversarial machine learning, and it works because computer vision doesn’t see what you see. A wrap that reads as loud graphic design to a person can read as nothing at all to a classifier.

Swearingen built it with a reinforcement learning model that grades its own homework. Pattern gets detected, model adjusts, tries again—what he described as teaching the model “how to paint.” It now spits out fresh patterns every minute, and he’s keeping the strongest ones offline so camera vendors can’t train against them.

“Privacy is a fundamental right,” he said, calling the patterns a way for people to “opt out of being tracked.” He said the idea took hold last year when he wanted to attend a protest and worried about the cameras logging everyone who showed up.

The long tail of hiding from machines

People have been improvising against detection systems for years, usually with hardware store solutions. San Francisco activists put traffic cones on the hoods of Waymo and Cruise robotaxis to freeze them in place, an exploit that needed no code at all.

During last year’s Los Angeles immigration raids, protesters went further and torched several Waymos. Masks, hoods, and brimmed caps remain the default on protest lines. Adversarial clothing labels have been selling face-confusing prints for years, and anti-recognition eyeglasses have arrived with thin evidence they do much.

What separates Swearingen’s project, which he calls noRecognition, is the target list. Swearingen tested against the specific stacks in wide deployment, and Flock is the one drawing heat. The company is facing a growing backlash on Capitol Hill, and internal documents show it pitched a plan to turn 350,000 Uber and Lyft dashcams into a rolling plate-scanning fleet.

Automated readers have already pulled over innocent drivers at gunpoint over bad matches, and immigrants and protesters keep getting swept into ICE’s AI dragnet. Lawmakers are pressing Meta over facial recognition in its smart glasses on a parallel track, so any legal measure to fight against automatic detection technology is being studied by privacy enthusiasts.

Swearingen’s noRecognition project is running a crowdfunding campaign to fund early merchandise—T-shirts and hoodies now, vehicle skins later. Swearingen said the goal is resolution high enough to work at a distance and design good enough that people will actually wear it.

Driving a wrapped car on public roads is its own legal question, and plate obstruction statutes vary by state. The patterns cover bodywork, not plates.

“Every failure improves my model, and so [the patterns] keep getting better and better,” Swearingen said.

Daily Debrief Newsletter

Start every day with the top news stories right now, plus original features, a podcast, videos and more.

Attribution

Originally reported by Decrypt

Get stories like this, daily.

Daily crypto + regulation intelligence, straight to your inbox. Free.

Notícias Relacionadas