{"id":1004881,"date":"2026-08-17T04:02:19","date_gmt":"2026-08-17T08:02:19","guid":{"rendered":"http:\/\/berndpulch.org\/?p=1004881"},"modified":"2026-08-17T04:02:22","modified_gmt":"2026-08-17T08:02:22","slug":"the-invisible-car-how-one-researcher-is-beating-flock-cameras-with-ai-generated-patterns","status":"publish","type":"post","link":"https:\/\/berndpulch.org\/de\/2026\/08\/17\/the-invisible-car-how-one-researcher-is-beating-flock-cameras-with-ai-generated-patterns\/","title":{"rendered":"The Invisible Car: How One Researcher Is Beating Flock Cameras with AI-Generated Patterns"},"content":{"rendered":"
\"\"<\/figure>\n\n\n\n

The Invisible Car: How One Researcher Is Beating Flock Cameras with AI-Generated Patterns

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—

Cybersecurity researcher Bill Swearingen has developed a vehicle wrap that can blind automatic license plate readers\u2014including Flock Safety’s controversial surveillance cameras. After 31 million tests, his AI-generated “adversarial patterns” can make a car virtually invisible to detection algorithms, potentially allowing drivers to evade the mass surveillance networks that now blanket thousands of American cities.

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The Man Behind the Mission

Bill Swearingen is a cybersecurity researcher and co-founder of SecKC, a Kansas City-based cybersecurity meetup. He describes himself as a middle-aged white guy who lives in the center of the United States and acknowledges that, as a result, he has not faced hardship or discrimination for who he is or what he looks like.

But last year, Swearingen wanted to attend a protest\u2014and felt uncomfortable. He was concerned that the vast number of cameras could track people exercising their constitutional rights to free expression. If he felt this way, he reasoned, others would as well\u2014including those who wanted to exercise their rights but may not feel safe or comfortable doing so.

So he got to work.

“Privacy is a fundamental right,” Swearingen told TechCrunch. “We never opted in to being watched.”

—

31 Million Tests Later

Swearingen spent a year running the same test, over and over again. The goal: produce a computer-generated pattern that could block surveillance cameras from detecting it.

Some 31 million tests later, 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\u2014from people to vehicles.

His project is called noRecognition.

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How It Works

The 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\u2014until someone knows where to look.”

To create the patterns, Swearingen used a reinforcement learning model that trained itself on patterns that evade detection. The model got to the point of being able to confuse all 11 open-source detection algorithms he tested, in addition to Flock’s license plate readers and even Axon’s body-worn cameras used by police departments.

—

The Def Con Demonstration

At this year’s Def Con cybersecurity conference in Las Vegas, Swearingen showed off a 2009 Toyota Yaris wrapped in vinyl featuring the chaotic, computer-generated pattern.

The demonstration was a success.

“We proved it was effective,” Swearingen told TechCrunch.

The Yaris drove past a Flock camera. The camera could see the car\u2014but it did not classify it as a car. Flock’s system, which can identify a vehicle by its make, model, color, body style, damage, roof racks, decals, and other visible details, was completely fooled.

The wrap didn’t make the car invisible to the human eye. It didn’t blind the camera. It simply made the car unrecognizable to the algorithms that power modern surveillance systems.

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Why This Matters

Swearingen’s breakthrough comes at a time when public opposition to Flock Safety’s surveillance network is reaching a fever pitch.

Flock now operates more than 120,000 cameras across 49 states, scanning roughly 20 billion license plates per month. Cities are canceling contracts, citizens are vandalizing cameras, and privacy advocates are warning of a surveillance state.

Swearingen’s vehicle wrap offers a potential technological countermeasure\u2014a way for individuals to opt out of being tracked.

—

Beyond Vehicles: T-Shirts and Hoodies

The noRecognition project extends beyond vehicles. Swearingen has also developed T-shirts and hoodies that feature algorithmic detection-busting patterns. He hopes to eventually put pattern-printed skins for entire cars on sale as well.

The effort is part of a broader trend of privacy advocates fighting back against the proliferation of surveillance technology.

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The Bigger Picture

Swearingen’s work raises a fundamental question: In an age of algorithmic surveillance, who gets to decide who is watched?

The patterns do not make anyone invisible. They simply restore a form of anonymity that surveillance systems have systematically eroded. For Swearingen, this is not about helping criminals evade detection. It is about protecting the right to privacy\u2014and the right to assemble without being tracked.

“Privacy is a fundamental right.”

For now, the noRecognition patterns are a proof of concept. But as surveillance networks continue to expand, the demand for countermeasures will only grow. The invisible car may soon be more than a curiosity\u2014it may be a necessity.

—

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Das unsichtbare Auto \u2013 Wie ein Forscher Flock-Kameras mit KI-Mustern austrickst

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—

Der Cybersicherheitsforscher Bill Swearingen hat eine Fahrzeugfolie entwickelt, die automatische Kennzeichenleser blenden kann \u2013 darunter auch die umstrittenen \u00dcberwachungskameras von Flock Safety. Nach 31 Millionen Tests k\u00f6nnen seine KI-generierten “adversarial patterns” ein Auto f\u00fcr Detektionsalgorithmen praktisch unsichtbar machen und so das Ausweichen vor den Massen\u00fcberwachungsnetzwerken erm\u00f6glichen, die inzwischen Tausende amerikanische St\u00e4dte \u00fcberziehen.

—

Der Mann hinter der Mission

Bill Swearingen ist Cybersicherheitsforscher und Mitbegr\u00fcnder von SecKC, einem Cybersicherheits-Treffpunkt in Kansas City. Er beschreibt sich selbst als einen wei\u00dfen Mittvierziger, der im Zentrum der USA lebt, und r\u00e4umt ein, dass er aufgrund seiner Person nie Diskriminierung oder Not erfahren hat.

Aber letztes Jahr wollte Swearingen an einer Protestveranstaltung teilnehmen \u2013 und f\u00fchlte sich unwohl. Er bef\u00fcrchtete, dass die Vielzahl von Kameras Menschen verfolgen k\u00f6nnte, die ihr verfassungsm\u00e4\u00dfiges Recht auf freie Meinungs\u00e4u\u00dferung aus\u00fcben. Wenn er sich so f\u00fchlte, so seine \u00dcberlegung, w\u00fcrden andere sich ebenso f\u00fchlen \u2013 darunter diejenigen, die ihre Rechte aus\u00fcben wollten, sich dabei aber nicht sicher oder wohl f\u00fchlten.

Also machte er sich an die Arbeit.

“Privatsph\u00e4re ist ein Grundrecht”, sagte Swearingen gegen\u00fcber TechCrunch. “Wir haben nie zugestimmt, beobachtet zu werden.”

—

31 Millionen Tests sp\u00e4ter

Swearingen verbrachte ein Jahr damit, immer denselben Test zu wiederholen. Das Ziel: ein computergeneriertes Muster zu entwickeln, das \u00dcberwachungskameras daran hindert, es zu erkennen.

Nach etwa 31 Millionen Tests kann er nun Muster auf Abruf produzieren, die, auf Kleidung und Gegenst\u00e4nde aufgebracht, einige der am h\u00e4ufigsten eingesetzten Kennzeichenleser und \u00dcberwachungskameras daran hindern, das Muster zu erkennen \u2013 von Menschen bis hin zu Fahrzeugen.

Sein Projekt hei\u00dft noRecognition.

—

Wie es funktioniert

Die Muster blockieren nicht die Aufzeichnung von Videomaterial durch \u00dcberwachungskameras. Stattdessen verhindern sie die F\u00e4higkeit der Kamera, Objekte, Personen oder Gesichter zu identifizieren, sodass die Kameras keine Detektionswarnungen ausl\u00f6sen.

“Indem man die F\u00e4higkeit der Kamera blockiert, das Muster zu erkennen, wird die Person wieder zur Nadel im Heuhaufen \u2013 bis jemand wei\u00df, wo er suchen muss.”

Um die Muster zu erstellen, verwendete Swearingen ein Reinforcement-Learning-Modell, das sich selbst darauf trainierte, Muster zu finden, die der Erkennung entgehen. Das Modell war schlie\u00dflich in der Lage, alle 11 Open-Source-Erkennungsalgorithmen, die er testete, sowie Flocks Kennzeichenleser und sogar Axons am K\u00f6rper getragene Kameras der Polizei zu t\u00e4uschen.

—

Die Def-Con-Demonstration

Auf der diesj\u00e4hrigen Def Con, der gr\u00f6\u00dften Cybersicherheitskonferenz der Welt in Las Vegas, f\u00fchrte Swearingen einen 2009er Toyota Yaris vor, der mit einer Vinylfolie mit dem chaotischen, computergenerierten Muster beklebt war.

Die Demonstration war ein Erfolg.

“Wir haben bewiesen, dass es funktioniert”, sagte Swearingen gegen\u00fcber TechCrunch.

Der Yaris fuhr an einer Flock-Kamera vorbei. Die Kamera konnte das Auto sehen \u2013 aber sie klassifizierte es nicht als Auto. Flocks System, das ein Fahrzeug anhand von Marke, Modell, Farbe, Karosserieform, Sch\u00e4den, Dachgep\u00e4cktr\u00e4gern, Aufklebern und anderen sichtbaren Details identifizieren kann, war v\u00f6llig get\u00e4uscht.

Die Folie machte das Auto f\u00fcr das menschliche Auge nicht unsichtbar. Sie blendete die Kamera nicht. Sie machte das Auto f\u00fcr die Algorithmen, die moderne \u00dcberwachungssysteme antreiben, einfach nicht erkennbar.

—

Warum das wichtig ist

Swearingens Durchbruch kommt zu einem Zeitpunkt, an dem der \u00f6ffentliche Widerstand gegen das \u00dcberwachungsnetz von Flock Safety einen H\u00f6hepunkt erreicht.

Flock betreibt inzwischen mehr als 120.000 Kameras in 49 Bundesstaaten und scannt etwa 20 Milliarden Kennzeichen pro Monat. St\u00e4dte k\u00fcndigen Vertr\u00e4ge, B\u00fcrger verw\u00fcsten Kameras, und Datensch\u00fctzer warnen vor einem \u00dcberwachungsstaat.

Swearingens Fahrzeugfolie bietet eine potenzielle technologische Gegenma\u00dfnahme \u2013 eine M\u00f6glichkeit f\u00fcr Einzelpersonen, sich der Verfolgung zu entziehen.

—

\u00dcber Fahrzeuge hinaus: T-Shirts und Hoodies

Das noRecognition-Projekt geht \u00fcber Fahrzeuge hinaus. Swearingen hat auch T-Shirts und Hoodies entwickelt, die algorithmische Detektionsmuster aufweisen. Er hofft, irgendwann auch bedruckte Folien f\u00fcr ganze Autos zum Verkauf anbieten zu k\u00f6nnen.

Die Bem\u00fchungen sind Teil eines breiteren Trends von Datenschutzaktivisten, die sich gegen die Verbreitung von \u00dcberwachungstechnologie wehren.

—

Das gro\u00dfe Ganze

Swearingens Arbeit wirft eine grundlegende Frage auf: Wer entscheidet im Zeitalter der algorithmischen \u00dcberwachung, wer beobachtet wird?

Die Muster machen niemanden unsichtbar. Sie stellen lediglich eine Form der Anonymit\u00e4t wieder her, die \u00dcberwachungssysteme systematisch ausgeh\u00f6hlt haben. F\u00fcr Swearingen geht es nicht darum, Kriminellen bei der Flucht zu helfen. Es geht darum, das Recht auf Privatsph\u00e4re zu sch\u00fctzen \u2013 und das Recht, sich zu versammeln, ohne verfolgt zu werden.

“Privatsph\u00e4re ist ein Grundrecht.”

Die noRecognition-Muster sind derzeit ein Proof of Concept. Aber da die \u00dcberwachungsnetze weiter wachsen, wird die Nachfrage nach Gegenma\u00dfnahmen nur steigen. Das unsichtbare Auto k\u00f6nnte bald mehr sein als eine Kuriosit\u00e4t \u2013 es k\u00f6nnte eine Notwendigkeit werden.

—

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Cybersecurity researcher Bill Swearingen develops AI-generated vehicle wrap that evades Flock Safety license plate readers and surveillance cameras.<\/p>","protected":false},"author":20155506,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[51904107,51186955,965824,5648,13788303],"tags":[182380370,242991666,791511900,310397,178620586,25380100,721667369,791511547,791511647,753228],"class_list":["post-1004881","post","type-post","status-publish","format-standard","hentry","category-bernd-pulch-2","category-cyber-stasi","category-cybersecurity","category-freedom","category-mass-surveillance","tag-ai-patterns","tag-bill-swearingen","tag-cybersecurity","tag-def-con","tag-flock-safety","tag-license-plate-readers","tag-norecognition","tag-privacy","tag-surveillance","tag-vehicle-wrap"],"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p1k3PD-4dpL","jetpack-related-posts":[{"id":1004192,"url":"https:\/\/berndpulch.org\/de\/2026\/08\/11\/flock-safetys-condor-cameras-track-people-not-just-cars-surveillance-expands-beyond-license-plates\/","url_meta":{"origin":1004881,"position":0},"title":"Flock Safety’s Condor Cameras Track People, Not Just Cars \u2013 Surveillance Expands Beyond License Plates","author":"Bernd Pulch","date":"August 11, 2026","format":false,"excerpt":"Flock Safety's Condor cameras now track people, not just cars. 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