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Analyzing YouTube's Audio Fingerprinter
Al Benedetto writes "I stumbled across this article which analyzes the YouTube audio content identification system in-depth. Apparently, since YouTube's system has no transparency, the behaviors had to be determined based on dozens of trial-and-error video uploads. The author tries things like speed/pitch adjustment, the addition of background noise, as well as other audio tweaks to determine exactly what you'd need to adjust before the fingerprinter started mis-identifying material. From the article: 'When I muted the beginning of the song up until 0:30 (leaving the rest to play) the fingerprinter missed it. When I kept the beginning up until 0:30 and muted everything from 0:30 to the end, the fingerprinter caught it. That indicates that the content database only knows about something in the first 30 seconds of the song. As long as you cut that part off, you can theoretically use the remainder of the song without being detected. I don't know if all samples in the content database suffer from similar weaknesses, but it's something that merits further research.'"
I didn't read TFA, but why should I care how YouTube does this??? It's not any kind of AI breakthrough, and the only reason to subvert the system is to do something illegal...
Yeah, wouldn't it be awesome if Google were forced to give away all their technology regardless of the investment they made to develop it? Sure, they'd lose out in every way, but who cares? All they did was put in the work, but they're a big corporation and you're a noble free hacker. You should win by default.