The rise of public surveillance has also led to the rise of an adversarial counterpart: fashion designed to confuse AI security cameras. It’s designed to baffle the senses of Axon body cams, Flock cameras, and other tools of mass surveillance through tricks like patterns that AI sensors confuse for animals, objects, or reflective fabric. A new limited-edition, crowdfunded clothing brand promises garments with patterns good enough to scramble 11 computer vision models. It’s called noRecognition. [Image: courtesy noRecognition] Bill Swearingen, a former chief intelligence officer and founder of the monthly Kansas City security meetup SecKC, created noRecognition after running 31.7 million digital tests to determine what kinds of patterns confuse multiple models at once, with the goal of creating a universal adversarial camouflage. He used AI throughout the process, including building and training a model, using that model to generate and test adversarial pattern geometry, and determining which patterns work best. For Swearingen, the challenge is that a pattern that beats one model often won’t beat several different ones. “I have beat every model I have tested, so beating a single model is a solved problem for me,” he says. “The search now is finding that one pattern that works across many models at once.” He unveiled noRecognition publicly at the Def Con cybersecurity conference in Las Vegas on August 7. A Kickstarter campaign he launched the same week to raise $5,000 has now raised more than $40,000. The money will go to fabric, cameras, and compute, says Swearingen, who calls the generosity and response “humbling.” [Image: courtesy noRecognition] The limited-edition clothing line includes a buff that can be worn as a neck gaiter, a T-shirt, and a sweatshirt, plus stickers and patches. There’s a 50-item run of each, and each will have a pattern generated for a single person. Swearingen’s website shows examples of some of the patterns the noRecognition model gen

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