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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.'"
There's the open-source library - libOFA - developed by Music IP (http://code.google.com/p/musicip-libofa/) which happens to create PUIDs on the first 135 seconds of audio in a track. It's used in the music-IP mixer (for mood mixes) but is also used by music database projects such as MusicBrainz.
From what I've seen, it's pretty decent audio fingerprinting, but I'm sure would be subject to the same limitations- if you remove the first 30 seconds of a clip- it would produce a very different fingerprint.
There's no reason to believe youtube isn't using this library or a derivative. There's also no reason to believe this result isn't intended. If the first 30 seconds of a song are missing- maybe that makes youtube confident that it could be considered fairuse.
Either way, I could imagine creating a fingerprint based on different sections of a song has the same problems doing an MD5 hash would- each fingerprint would be entirely different. If you don't just compare bit-to-bit, it'll be impossible to catch ALL permutations. And the fact is, that's a lot of computing power anyhow.
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It's a good thing no one at Youtube reads Slashdot. Otherwise they might come up with a fix! So, everyone keep this a secret! SHHHH!
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Here's an idea. Start out the video with a useless narrative for the first thirty seconds "blah blah blah skip until :30 and ignore this intro blah blah" then start the music. That way everybody is happy. All google employees are too elitist to read slashdot, right?
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This seems like a good time to pump my own open source project: pHash. pHash is a perceptual hashing library that computes hashes for audio, video and image files, with text and PDF hashing coming soon. We use an algorithm similar to YouTube's audio fingerprinting method but we do not only take into account the first 30 seconds. Although, it's impossible to tell from this basic test whether their algorithm truly only looks at the first 30 seconds, or if the algorithm considers them to be different audio files. If the song is only 1 minute in duration, and 30 seconds is blank, is that really the same audio file as the full 1 minute version? At some point the audio files are not really the same anymore, although the perceptual hashes should be somewhat close to each other. Please give pHash a try. We could use some feedback from the OSS community and would appreciate it greatly.
And who fingerprints the analyzers who analyze the analyzers?
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An unfortunate result. The last 30 seconds of most songs are not usually as interesting as the first 30 seconds.
I wonder if he tried mangling the first 30 seconds at all. For example, keep the first 5 seconds, mess up the 6th and 7th seconds, and then continue on. Or perhaps adding in a base line that would be hard to hear. Or something at the high end of the audio frequency spectrum, to annoy all those teenagers while I listen to my free music in peace.
This is not riddle posed by Nature
This is one of the wonderful things about science: it doesn't matter where the puzzle comes from, the same techniques work to solve it.
Reverse engineering of this kind is one of the most useful areas of applied science, and it is as much research as any other area of scientific enquiry. It is frequently the case that there are many ways to find the answer to a puzzle, and this guy has chosen one of them based on the resources he has available. More power to him for demonstrating how good science can be used to discover what others want to keep secret.
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