{"id":3491611,"date":"2026-08-09T14:00:00","date_gmt":"2026-08-09T14:00:00","guid":{"rendered":"https:\/\/techingeek.com\/index.php\/2026\/08\/09\/this-oppositional-pattern-may-hinder-surveillance-cameras-from-recognizing-you\/"},"modified":"2026-08-09T14:00:00","modified_gmt":"2026-08-09T14:00:00","slug":"this-oppositional-pattern-may-hinder-surveillance-cameras-from-recognizing-you","status":"publish","type":"post","link":"https:\/\/techingeek.com\/index.php\/2026\/08\/09\/this-oppositional-pattern-may-hinder-surveillance-cameras-from-recognizing-you\/","title":{"rendered":"This \u2018oppositional\u2019 pattern may hinder surveillance cameras from recognizing you"},"content":{"rendered":"
\n

Bill Swearingen has dedicated the last year to conducting nearly identical tests repeatedly. The objective was to create a computer-generated design that could inhibit the surveillance cameras positioned along America\u2019s streets from recognizing it.<\/p>\n

After conducting approximately 31 million tests, Swearingen claims he is now able to generate designs on-demand that, when applied to apparel and objects, obstruct some of the most widely utilized license plate readers and surveillance cameras from noticing whatever the design conceals, whether it’s people or vehicles.<\/p>\n

His initiative, referred to as noRecognition, empowers individuals to evade the automatic detection and algorithmic surveillance employed across the U.S. and elsewhere.<\/p>\n

In recent times, surveillance cameras have been enhanced with the capability to recognize occurrences in the recorded footage, ranging from tracking the license plates of fast-moving vehicles to employing facial recognition to identify alleged criminals, though not without mixed results and occasionally alarming outcomes. The detection algorithms that drive most surveillance cameras today can analyze extensive quantities of footage, enabling law enforcement to isolate activities of interest, similar to extracting a needle from a haystack.<\/p>\n

Swearingen\u2019s computer-generated designs do not prevent surveillance cameras from capturing video footage. Instead, they disrupt the camera\u2019s capacity to recognize objects, individuals, or faces, ensuring that the cameras do not trigger any detection alerts. By impeding the camera\u2019s ability to perceive what the design obscures, the individual becomes a needle in a haystack once more \u2014 until someone knows where to search.\u00a0<\/p>\n

\u201cPrivacy is a fundamental right,\u201d Swearingen informed TechCrunch during a call this week. He characterized his designs as a means for individuals to \u201copt-out of being tracked.\u201d<\/p>\n

During its initial public demonstration on Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen successfully showcased the design printed on a vehicle, confirming that these designs can effectively thwart surveillance detection in real-world scenarios.<\/p>\n

Instructing a model in the art of painting<\/h2>\n

In a call from his residence in Kansas City, where he co-founded the cybersecurity meet-up SecKC, Swearingen told TechCrunch that as a cybersecurity expert, he is keenly aware of the privacy and security threats posed by surveillance.<\/p>\n

He recounted how his city is inundated with surveillance cameras, often situated mere feet apart. He noted that neither he nor others consented to being monitored, just as he never agreed to the government using his driver\u2019s license for facial recognition.<\/p>\n

Swearingen depicted himself as a middle-aged white man residing in the heart of the United States, and acknowledged that, as a result, he has not encountered adversity or discrimination due to his identity or appearance. He shared how the previous year he considered attending a protest but felt uneasy, worrying that the numerous cameras could monitor individuals exercising their constitutional rights to free speech.<\/p>\n

If he felt this way, it\u2019s certain that others would too, including those wishing to assert their rights who may not feel secure or comfortable doing so. Swearingen took action.<\/p>\n

\"a
Image Credits:<\/strong>Bill Swearingen<\/span><\/figcaption><\/figure>\n

Throughout the history of cameras capable of detection, there have been attempts to counter the technology. Various art projects and clothing brands have introduced garments designed to aid individuals in evading facial recognition. Some eyewear manufacturers are also entering this movement, albeit with limited effectiveness.\u00a0<\/p>\n

Swearingen mentioned that his research builds on some of this prior work, which indicated that blocking camera detections was achievable.\u00a0<\/p>\n

He commenced last year with a proof-of-concept test lab, which began by progressively overcoming one open-source video camera detection algorithm at a time. Over the course of the year, he enhanced the designs by increasing his tests with supplementary computing power. He expressed gratitude to the broader community that contributed hardware to advance the project.\u00a0<\/p>\n

His proof-of-concept gradually evolved into a reinforcement learning model, essentially a self-sufficient system that could autonomously educate itself on which designs are effective and which are not against the particular camera algorithms he is evaluating. In layman’s terms, Swearingen explained to TechCrunch that he effectively taught his model \u201chow to paint.\u201d<\/p>\n

Each time a design was unsuccessful and an algorithm recognized it, the model would retry, repeatedly, until it eventually outsmarted multiple algorithms simultaneously.<\/p>\n

His model soon began to discover optimal combinations of designs capable of defeating all 11 open-source detection algorithms he evaluated, including the software underlying Flock license plate readers, Axon body-worn cameras, and cameras running Clearview AI.<\/p>\n

Currently, the model generates fresh designs every minute, with each batch mathematically superior to the last, he remarked.<\/p>\n

On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen conducted his first real-world trial. With collaboration from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen\u2019s latest designs to determine if the vehicle would evade detection by a Flock camera.<\/p>\n

\u201cWe established it was effective;\u201d Swearingen stated, although he noted that the wheels presented a challenge. The video of the demonstration will be released in the coming weeks, according to Donut Media.<\/p>\n

With a public demonstration in Las Vegas successfully completed, the project serves as preliminary evidence that evading algorithmic detection in public spaces is feasible. The next phase involves putting the designs into the hands of interested individuals, he mentioned.<\/p>\n

The noRecognition initiative also has a crowdsourcing campaign to help finance the distribution of early merchandise showcasing the designs, ranging from T-shirts to hoodies, with aspirations for pattern-printed skins for automobiles in the future. Swearingen expressed that the goal is for the designs to be of high quality and resolution sufficient to function from a distance while also appearing aesthetically appealing.<\/p>\n

He stated that he is withholding his most effective designs from the internet to hinder camera manufacturers from countering them, yet acknowledged that the work remains ongoing. His models continue to generate new designs.<\/p>\n

\u201cEvery setback enhances my model, and thus [the designs] keep evolving to be better and better,\u201d he remarked.<\/p>\n

\"a
A photo of a 2009 Toyota Yaris at the Def Con conference in Las Vegas, covered in a pattern made by Bill Swearingen, as part of a test to see if it can defeat surveillance camera detection.<\/span>Image Credits:<\/strong>Bill Swearingen \/ Donut Media (used with permission)<\/span><\/figcaption><\/figure>\n<\/div>\n

When you purchase through links in our articles, we may earn a small commission. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

\n

Bill Swearingen has dedicated the last year to conducting nearly identical tests repeatedly. The objective was to create a computer-generated design that could inhibit the surveillance cameras positioned along America\u2019s streets from recognizing it.<\/p>\n

After conducting approximately 31 million tests, Swearingen claims he is now able to generate designs on-demand that, when applied to apparel and objects, obstruct some of the most widely utilized license plate readers and surveillance cameras from noticing whatever the design conceals, whether it’s people or vehicles.<\/p>\n

His initiative, referred to as noRecognition, empowers individuals to evade the automatic detection and algorithmic surveillance employed across the U.S. and elsewhere.<\/p>\n

In recent times, surveillance cameras have been enhanced with the capability to recognize occurrences in the recorded footage, ranging from tracking the license plates of fast-moving vehicles to employing facial recognition to identify alleged criminals, though not without mixed results and occasionally alarming outcomes. The detection algorithms that drive most surveillance cameras today can analyze extensive quantities of footage, enabling law enforcement to isolate activities of interest, similar to extracting a needle from a haystack.<\/p>\n

Swearingen\u2019s computer-generated designs do not prevent surveillance cameras from capturing video footage. Instead, they disrupt the camera\u2019s capacity to recognize objects, individuals, or faces, ensuring that the cameras do not trigger any detection alerts. By impeding the camera\u2019s ability to perceive what the design obscures, the individual becomes a needle in a haystack once more \u2014 until someone knows where to search.\u00a0<\/p>\n

\u201cPrivacy is a fundamental right,\u201d Swearingen informed TechCrunch during a call this week. He characterized his designs as a means for individuals to \u201copt-out of being tracked.\u201d<\/p>\n

During its initial public demonstration on Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen successfully showcased the design printed on a vehicle, confirming that these designs can effectively thwart surveillance detection in real-world scenarios.<\/p>\n

Instructing a model in the art of painting<\/h2>\n

In a call from his residence in Kansas City, where he co-founded the cybersecurity meet-up SecKC, Swearingen told TechCrunch that as a cybersecurity expert, he is keenly aware of the privacy and security threats posed by surveillance.<\/p>\n

He recounted how his city is inundated with surveillance cameras, often situated mere feet apart. He noted that neither he nor others consented to being monitored, just as he never agreed to the government using his driver\u2019s license for facial recognition.<\/p>\n

Swearingen depicted himself as a middle-aged white man residing in the heart of the United States, and acknowledged that, as a result, he has not encountered adversity or discrimination due to his identity or appearance. He shared how the previous year he considered attending a protest but felt uneasy, worrying that the numerous cameras could monitor individuals exercising their constitutional rights to free speech.<\/p>\n

If he felt this way, it\u2019s certain that others would too, including those wishing to assert their rights who may not feel secure or comfortable doing so. Swearingen took action.<\/p>\n

\"a
Image Credits:<\/strong>Bill Swearingen<\/span><\/figcaption><\/figure>\n

Throughout the history of cameras capable of detection, there have been attempts to counter the technology. Various art projects and clothing brands have introduced garments designed to aid individuals in evading facial recognition. Some eyewear manufacturers are also entering this movement, albeit with limited effectiveness.\u00a0<\/p>\n

Swearingen mentioned that his research builds on some of this prior work, which indicated that blocking camera detections was achievable.\u00a0<\/p>\n

He commenced last year with a proof-of-concept test lab, which began by progressively overcoming one open-source video camera detection algorithm at a time. Over the course of the year, he enhanced the designs by increasing his tests with supplementary computing power. He expressed gratitude to the broader community that contributed hardware to advance the project.\u00a0<\/p>\n

His proof-of-concept gradually evolved into a reinforcement learning model, essentially a self-sufficient system that could autonomously educate itself on which designs are effective and which are not against the particular camera algorithms he is evaluating. In layman’s terms, Swearingen explained to TechCrunch that he effectively taught his model \u201chow to paint.\u201d<\/p>\n

Each time a design was unsuccessful and an algorithm recognized it, the model would retry, repeatedly, until it eventually outsmarted multiple algorithms simultaneously.<\/p>\n

His model soon began to discover optimal combinations of designs capable of defeating all 11 open-source detection algorithms he evaluated, including the software underlying Flock license plate readers, Axon body-worn cameras, and cameras running Clearview AI.<\/p>\n

Currently, the model generates fresh designs every minute, with each batch mathematically superior to the last, he remarked.<\/p>\n

On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen conducted his first real-world trial. With collaboration from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen\u2019s latest designs to determine if the vehicle would evade detection by a Flock camera.<\/p>\n

\u201cWe established it was effective;\u201d Swearingen stated, although he noted that the wheels presented a challenge. The video of the demonstration will be released in the coming weeks, according to Donut Media.<\/p>\n

With a public demonstration in Las Vegas successfully completed, the project serves as preliminary evidence that evading algorithmic detection in public spaces is feasible. The next phase involves putting the designs into the hands of interested individuals, he mentioned.<\/p>\n

The noRecognition initiative also has a crowdsourcing campaign to help finance the distribution of early merchandise showcasing the designs, ranging from T-shirts to hoodies, with aspirations for pattern-printed skins for automobiles in the future. Swearingen expressed that the goal is for the designs to be of high quality and resolution sufficient to function from a distance while also appearing aesthetically appealing.<\/p>\n

He stated that he is withholding his most effective designs from the internet to hinder camera manufacturers from countering them, yet acknowledged that the work remains ongoing. His models continue to generate new designs.<\/p>\n

\u201cEvery setback enhances my model, and thus [the designs] keep evolving to be better and better,\u201d he remarked.<\/p>\n

\"a
A photo of a 2009 Toyota Yaris at the Def Con conference in Las Vegas, covered in a pattern made by Bill Swearingen, as part of a test to see if it can defeat surveillance camera detection.<\/span>Image Credits:<\/strong>Bill Swearingen \/ Donut Media (used with permission)<\/span><\/figcaption><\/figure>\n<\/div>\n

When you purchase through links in our articles, we may earn a small commission. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n","protected":false},"author":2,"featured_media":3491612,"comment_status":"open","ping_status":"closed","sticky":false,"template":"Default","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3491611","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts\/3491611"}],"collection":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/comments?post=3491611"}],"version-history":[{"count":0,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts\/3491611\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/media\/3491612"}],"wp:attachment":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/media?parent=3491611"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/categories?post=3491611"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/tags?post=3491611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}