GoKawiilA camera and its software labeled Bill Swearingen as a person. Then it suddenly wasn’t sure — all because of some weird pattern that he held up to disguise himself.
I watched it happen from my seat at annual hacker convention Defcon. Swearingen, a longtime cybersecurity professional and founder of the Kansas City security community SecKC, stood onstage in front of a live camera feed as a person-detection system analyzed him. On the giant screen behind him, the software’s confidence score cleared 0.75, the threshold it needed to declare that, yes, there was a human being in the frame.
Then Swearingen raised a flat panel covered in a bizarre black-and-white pattern. The score started falling. It slipped below the threshold, eventually landing at 0.21.
“No person detected,” the screen announced in bright green letters.
It felt like a low-budget magic trick. Swearingen was still standing there, plainly visible to everyone in the room. The software was still receiving the camera image, but it no longer detected a person above the configured confidence threshold.
Swearingen has spent the past year searching for patterns that can confuse the computer-vision systems used to identify people. His project is called noRecognition, and its end goal is to create clothing that makes the wearer harder for AI surveillance systems to detect. It’s a fascinating project, but it’s still a work in progress.
The camera wasn’t trying to identify him
We tend to call this kind of technology “facial recognition,” but surveillance systems can involve several separate layers of AI-based detection.
A person detector asks whether a human body is in the frame. A face detector finds and isolates a face. Facial recognition then compares that face with a database and asks whether it knows who the person is.
Person detection, face detection and facial recognition perform different jobs, though each step can depend on the one before it.
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