Project noRecognition: Teaching AI to Fool Surveillance Cameras
Security researcher Bill Swearingen's noRecognition project uses 31 million tested patterns to defeat license plate reader and surveillance camera AI detection.
Kansas City researcher Bill Swearingen built noRecognition, using reinforcement learning across roughly 31 million tests to generate printed patterns that break the detection layer of license plate readers and surveillance cameras. The strongest validated result achieved 61.7% non-detection against a detector taken from a real deployed camera, though most headline figures remain digital simulations. At DEF CON he covered a 2009 Toyota Yaris in a new pattern and reported it effective against a Flock Safety camera, with curved wheels the main weak point. He is crowdfunding apparel products and withholding his best patterns to prevent camera makers from blocking them.
- Patterns disrupt detection layers, not camera recording; footage is still captured
- Reinforcement learning iterated patterns to evade multiple detection algorithms simultaneously
- 61.7% non-detection achieved against a real deployed camera's detector in simulation
- DEF CON demo: Toyota Yaris wrapped in pattern evaded a Flock Safety camera
- Best patterns unpublished; monetized via crowdfunded T-shirts and hoodies
- Vendors
- Flock SafetyDonut Media
- Products
- noRecognition
- Countries
- United States
From the original article
3 min read at securityaffairs.com
Researchers tested 31 million patterns to disrupt surveillance AI, with promising results but significant gaps between simulation and real-world use. The Kansas City-based cybersecurity researcher Bill Swearingen spent the past year doing something that sounds almost too simple to work: printing patterns, watching cameras fail to detect them, and repeating.
By @securityaffairs · securityaffairs.com
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