‘Invisibility cloak’ that hides wearers from security cameras invented by students in Wuhan

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Students in China have invented an “invisibility cloak” designed to hide wearers from security cameras.

The InvisDefense, developed by a team at Wuhan University, is a low-cost coat that can be used day or night to evade detection from security camera systems monitored by artificial intelligence.

During the day, the coat uses a pattern to effectively blind the cameras, while at night it emits unusual heat signals to confuse infrared cameras.

The coat appears ordinary to human eyes and is ineffective at hiding wearers from human-monitored security cameras. Its design means it would also potentially confuse some of the detection systems used in self-driving cars, but not the human drivers.

“Nowadays, many surveillance devices can detect human bodies,” Professor Wang Zheng from Wuhan University’s school of computer science, who oversaw the invention, told the South China Morning Post.

“Cameras on the road have pedestrian detection functions and smart cars can identify pedestrians, roads and obstacles. Our Invisdefense allows the camera to capture you, but it cannot tell if you are human.”

The coat won first prize in a contest sponsored by Huawei Technologies, with a paper describing the technology set to be presented at the AAAI 2023 AI conference in the US in February.

Wuhan University PhD student Wei Hui, who created the algorithm that generated the coat’s pattern, estimated that it costs less than 500 yuan (£59) to make the InvisDefense.

“The most difficult part is the balance of the camouflage pattern. Traditionally, researchers used bright images to interfere with machine vision and it did work,” said Wei Hui.

“But it stands out to human eyes, making the user even more conspicuous. We use algorithms to design the least conspicuous patterns that can disable computer vision.”

Professor Wang said the coat could be used for stealth military uniforms to evade detection from drones, or AI-controlled units on the battlefield.

Researchers could also potentially use the pattern-generating algorithm to better understand and patch up loopholes in artificial intelligence and computer recognition technology.