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Wow thank you for the detailed response. Seems like you are much smarter than I am! How do you know this would perform any better than what they are currently

by rifung 10y ago

Wow thank you for the detailed response. Seems like you are much smarter than I am! How do you know this would perform any better than what they are currently doing though? Do you think this would catch the cases which currently pass their filtering like videos with mirrored images? And would this produce less false positives? According to this post, it seems they already do something similar to what you've described: https://www.quora.com/Does-Content-ID-look-for-a-match-of-the-actual-code-or-a-match-of-the-audio-produced-by-the-code/answer/Dwayne-Charrington?srid=iHLF https://www.quora.com/Does-Content-ID-look-for-a-match-of-th... I suppose in the end this is all going to just be speculation since they haven't disclosed what they actually do to try to find copyrighted content. Can you even predict how well a machine learning or classification algorithm will do without just trying it empirically?

highd 10y ago
What you can say is if there are N videos and k types of transforms that are used to "hide" content this would be hypothetically perfect with O(n + k) train examples - naive "reporting only" would be O(nk). This means that you should just need one example of each transform and one known instance of a particular copyright infringing media in order to get high performance. Mirroring etc. would just be one example of the transforms an approach like this would be robust to. It's hard to say what the error rates will be, but you should be on the last 5-10% Pd/Pfa pretty much instantly and then if you want more you can just acquire more true labelled data (if a user can find a copyrighted video on your site, I would think a contractor could, too!).
- rifung 10y ago
  This well beyond what I understand so excuse me if I'm misunderstanding but doesn't this imply that they'd need to guess what k transforms might be used? This just seems like a cat and mouse type ordeal which I can only assume is something they would like to avoid as the complexity of and costs of running the system only gets worse, while the complexity does not add up on the side of those making pirated videos.

by rifung 10y ago

highd 10y ago

What you can say is if there are N videos and k types of transforms that are used to "hide" content this would be hypothetically perfect with O(n + k) train examples - naive "reporting only" would be O(nk). This means that you should just need one example of each transform and one known instance of a particular copyright infringing media in order to get high performance. Mirroring etc. would just be one example of the transforms an approach like this would be robust to. It's hard to say what the error rates will be, but you should be on the last 5-10% Pd/Pfa pretty much instantly and then if you want more you can just acquire more true labelled data (if a user can find a copyrighted video on your site, I would think a contractor could, too!).

rifung 10y ago
This well beyond what I understand so excuse me if I'm misunderstanding but doesn't this imply that they'd need to guess what k transforms might be used? This just seems like a cat and mouse type ordeal which I can only assume is something they would like to avoid as the complexity of and costs of running the system only gets worse, while the complexity does not add up on the side of those making pirated videos.