Submitted by **vyasnikhil96** t3_1190lw8
in **MachineLearning**

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**Battleagainstentropy**
t1_j9lpzkd wrote

This is really interesting work. I wonder how meaningful these metrics can be made. For example, if I write a book about Harry Potter, the expert mug maker, then your metric is x. If I write about Barry Blotter, the boy wizard at Smogwarts, then your metric is y. IANAL but I think that the value needed to prove derivative work is a question of fact that would be up to a jury to decide, so being able to explain such a metric to laypeople could make this work. It’s somewhat similar to the way that DNA testing required a certain amount of education for juries (one in a million match and all that)

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**vyasnikhil96**
OP
t1_j9ltbq3 wrote

I agree. Note that overall there are two things we can hope for: 1. Using this approach with a appropriate k removes most of the "obvious" copyright violations and 2. for the remaining images the value k can be interpreted to determine whether there was a copyright violation or not, where the interpretation will necessarily be application and context dependent.

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