I always thought that a near learning project would be training an ML on “real” cards and then detecting fakes. I don’t play the games but I was always thrown by how much effort went into counterfeits, but I guess there’s enough profit for someone. There’s usually something wrong with the registration or colors.
What is missing in the context here is that the cards mentioned in this article are not actually real. They never existed, and therefore they are not "counterfeits" of a real one, they are just made up. Someone just claimed to know someone that had playtest cards from back in the day. They are not a commercial product.
Avg player doesn't buy a few thousand cards at a time. If you buy a high value card from a random seller you should always check it unless you trust them from references.
People only pull out slower tools for valuable, forgery worthy cards.
If someone is buying 1000 $1000 dollar cards, it’s still worth it lol.
Even cheap forgeries cost money to produce, so I wouldn’t expect a lot of low value cards to be forged. If you sort out the valuable cards and do random sampling, you can probably catch the most problematic cases.
I built one of these several years ago for MtG cards. Trained a neural network with a binary classifier on a cheap $20 USB microscope looking at examples of the backs of real cards vs. fake cards.
Sadly never got around to shipping it, because it worked really well. Ported it to the web, but never figured out the billing issue, and so it died during the delivery phase. From time-to-time, I still wonder if I should resurrect this project, because I think it could help a lot of people.
