GoKawiilAI-powered cameras dot streets across the world, equipped with the power to identify faces or vehicle license plates. But a public backlash is gaining momentum.
Privacy concerns abound, encompassing the lack of consent for capturing data, how that data is stored and used, and the risk of misuse. Those concerns are motivating people to fight back. The DeFlock project, for instance, maps automated license plate readers (ALPRs) to raise awareness. Some people resort to extreme measures, such as vandalizing or damaging ALPRs. Others are stitching together more creative responses, crafting “adversarial fashion” to evade surveillance cameras, like a Kickstarter project called noRecognition, presented at last month’s DEF CON hacker convention.
Scrambling surveillance
In 2025, cybersecurity expert Bill Swearingen began experimenting with a simple Python-based fuzzer, a tool that provides invalid inputs to reveal software bugs, security vulnerabilities, or unexpected behavior. The fuzzer targeted one of the most popular object detection frameworks, called YOLO. He then developed what he’d learned into a reinforcement learning algorithm that generates various adversarial patterns, which he presented at DEF CON.
Each pattern is a colorful geometric abstraction he has tested against 11 object detection models—four that search faces, two that recognize faces, and five that detect people—most of which are publicly available. Successful patterns thwart the object-detection systems, lowering their confidence scores, sometimes even to the point of no detection.
“Privacy is a human right, and the popularity of this just goes to show that people are interested in preserving their privacy,” Swearingen says.
Cap_able and Urban Privacy are already selling physical garments. Cap_able’s patented manufacturing method weaves its bright and bold motifs into jacquard knitted fabrics. The ethically produced and sustainably made dresses, pants, and tops interfere with certain computer vision systems, particularly those backed by fast convolutional neural networks, which may lead them to classify wearers as animals or objects.
“If we’re able to camouflage a person as something else, then we’re obtaining our goal,” says Cap_able founder Rachele Didero, who’s also an assistant professor at the Free University of Bozen-Bolzano in Italy. “We use this very visible and tangible item to talk about something that most of the time is intangible.”
Meanwhile, Urban Privacy aims to baffle some facial recognition systems based on OpenCV algorithms with its latest Faception Reloaded collection. Black-and-white prints abstracted from a human face show up as additional faces on detectors, slowing them down. Asymmetrical cuts and wide silhouettes intend to conceal, making it harder to discern your body’s shape and gait. “The idea is to create false data,” says cofounder Daniel Preuß.
Simulated patterns of the kind intended to disrupt machine vision person detectors. noRecognition
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