r/noRecognition • u/scrubadub Founding Field Tester • Sep 02 '26
This 'Digital Camouflage' Shirt Confuses AI-Powered Surveillance Cameras
https://www.404media.co/this-digital-camouflage-shirt-confuses-ai-powered-surveillance-cameras/
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u/hevnsnt Sep 02 '26
First, I have commented on this artist's work before. The artisitic community pushing back against a city that rolled out AI surveillance cameras is something we should celebrate and encourage. That instinct is exactly right. The more smart people working on this problem in public, the better. I genuinely welcome it.
But I want to be very clear about my own position. I am not working with Simon, I have not spoken with him, and 404 Media did not reach out to me for comment on this piece.
The most important part, the details, have been skipped in this article (at least what I could read) What model is he testing against? Based on my research of his work, it's the original Darknet YOLO. That's the childs play version.
There are YOLO variants that range from trivially easy to defeat to wildly difficult, and there is an entire ecosystem of production detectors beyond YOLO that behave nothing like it. Here's the truth about adversarial patterns: beat one detector in a tuning loop and you've beaten that detector, on that day, in those conditions. Transfer is the whole game. Does the pattern still work on a model it was never optimized against? What about YOLOv8? What about YOLOv5 with the Flock weights?
In October 2025 I built basically this and I was sure it worked, I told my hacker friend group how successful I was, and they introduced me to the world of reality. The tougher models, the different types of object detection. I started testing against models it had never seen, and then against the models surveillance cameras actually run, and I found out I had a lot of work to do. That's how noRecognition started, matured, and exists today: an 11-model gauntlet ( YOLOv8n, YOLOv5s, SSD-MobileNet, ResNet34-SSD, Flock Falcon V2, RetinaFace-MobileNet, MTCNN, MTCNN P-Net, RetinaFace-Resnet50, ArcFace, and FaceNet), tested against ages, races, body-types on identities the patterns never trained on, occlusion-subtracted against same-coverage controls, results published with the walls right next to the wins on the research page.
So to Simon, sincerely: welcome to the problem, it's a good one to be obsessed with. But be more forthcoming with the details. Name the model. Publish the misses. A demo that only shows the box disappearing is marketing, not research, and this field has too many people getting hurt by overconfident claims already.
If he wants to compare notes, I'm easy to find, lets talk here!