[serf]

Maybe a certain percentage, but much of the work in detecting many counterfeit bags involves actually inspecting a lot of the work on the interior of the bag or looking for specific construction characteristics that knock-off groups don't take the time to replicate for a market that is unknowing.

source: a mom that was nuts about calling out fakes in a snarky manner.

[KGIII]

My thinking was that one might be able to do it with their cell phone, with close up images. Then it could maybe tell things like stitching, material, precise color match, and maybe then be able to say if it is counterfeit.

I'm just not sure ML is yet that good. My thoughts were more about multiple pictures or a small video. I'd suspect that it could train itself further as the DB expands.

I'm just not sure how good it actually can be. It probably only has to be better than a layperson, though I could see more precise stuff used to scan by customs. Any percentage higher than the normal buyer of such would be better, assuming few false positives.

[landon32]

You could certainly do that, but you would need to create the training dataset. It would require many thousands of photos of real and counterfeit bags up close.

Image recognition has been mostly solved by ML techniques, so if you just assemble the data and train the network you can distinguish any set of things that are significantly different visually.