Show HN: Ray-BANNED, Glasses to detect smart-glasses that have cameras

Summary

Glasses to detect smart-glasses that have cameras I'm experimenting with 2 main approaches: Optics: classify the camera using light reflections. Networking: bluetooth and wi-fi analysis. So far fingerprinting specific devices based on bluetooth (BLE) is looking like easiest and most reliable approach. The picture below is the first version, which plays the legend of zelda 'secret found' jingle when it detects a BLE advertisement from Meta Raybans. I'm essentially treating this README like a logbook, so it will have my current approaches/ideas. Optics By sending IR at camera lenses, we can take advantage of the fact that the CMOS sensor in a camera reflects light directly back at the source (called 'retro-reflectivity' / 'cat-eye effect') to identify cameras. This isn't exactly a new idea. Some researchers in 2005 used this property to create 'capture-resistant environments' when smartphones with cameras were gaining popularity. There's even some recent research (2024) that figured out how to classify individual cameras based on their retro-reflections. Now we have a similar situation to those 2005 researchers on our hands, where smart glasses with hidden cameras seem to be getting more popular. So I want to create a pair of glasses to identify these. Unfortunately, from what I can tell most of the existing research in this space records data with a camera and then uses ML, a ton of controlled angles, etc. to differentiate between normal reflective surfaces and cameras. I would feel pretty silly if my solution uses its own camera. So I'll be avoiding that. Instead I think it's likely I'll have to rely on being consistent with my 'sweeps', and creating a good classifier based on signal data. For example you can see here that the back camera on my smartphone seems to produce quick and large spikes, while the glossy screen creates a more prolonged wave. After getting to test some Meta Raybans, I found that this setup is not going to be sufficient. Here's a test of some ...