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salgernon | 1 year ago

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.

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FanaHOVA|1 year ago

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.

See here for a bit more background: https://www.cgccards.com/news/article/13347/

strstr|1 year ago

If you are willing to pull out a loupe you don’t really need ML. You can just look at the rosette patterns.

For Mtg cards, the green dot test is very easy to learn, and I’m not familiar with any fakes that pass it.

(Edit: arguably you have to worry about rebacking with the green dot test, but rebacking is typically pretty fishy looking.)

nilamo|1 year ago

Pulling out a loupe and manually inspecting a card is a slow process if you have a few thousand cards (avg player).

krisoft|1 year ago

> There’s usually something wrong with the registration or colors.

That can be selection bias too.

Maybe the counterfeits where there is nothing wrong with the registration of colours are just not recognised as counterfeits.

Similarly how seemingly every hacker you can hear about in the news are bad at opsec. Because you wouldn't hear about them if they weren't.

HanClinto|1 year ago

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.

https://youtu.be/6_kKR7YgPF4

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.