Outsmarting YouTube's Content ID: My Journey

Donsanti92

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In the past few months, my main focus has been on YouTube's Content ID system. While I believe I may have found a solution, it is not yet solid-proof. However, those who are knowledgeable about AI may be able to help us with the final piece of the puzzle.

We know that YouTube scans content and generates fingerprints for both the audio and video. These fingerprints are then stored in their database to compare against future duplicates. I am aware that creators often work with DRM companies, which act as intermediaries between creators and YouTube's Content ID system. These companies upload the creators' content and check for duplicates. If they find any, they share the revenue collected.

I have read some research papers on fingerprinting methods, which mention that the science still cannot detect some modifications when they are properly modified. I decided to investigate this further.

In my case, I only have problems with audio. Therefore, I concentrated on building my own fingerprinting tool to scan my modified audio sample against the original audio sample. Here are my results:

In these experiments I had two videos, which I will call X and Y.

1st Experiment, I used Video X:
I compared the original and modified audio samples using a fingerprinting technique. The results showed that the modified audio was 20% similar to the original. However, when I uploaded the video to a test channel, I received a claim when the video reached 100k views.

2nd Experiment, I used Video Y:
I then tested video Y with another layer of modifications on the audio, reducing the similarity to only 3% compared to the original audio. However, once again, the DRM company claimed my video after it received more than 130k views.

3rd Experiment I used Video X:
In my third experiment, I used the same video & audio from 1st experiment but added an extra modification to it, BOOM the claim was not made!
But the weird thing is that the similarity percentage with the original audio was 25%. This puzzled me, as the video received more than 700k views, they should've claimed it already!

Quick summary:
They claimed me when I had 20% similarity.
They claimed me when I had 3% similarity.

They didn’t claim me when I had 25% similarity which is the highest I had!

I continued with these experiments with different videos, but to sum up, it is still a hit-and-miss situation (30% success rate).
If anyone can provide more insight, it would be greatly appreciated.

PS: I know some people may think this is a lot of work and may ask why I can't use 100% original content. I have other channels where I use 100% original content, but blackhat STILL ROCKS my friend.
 
that would be such a game changer knowing how to bypass audio id, I guess that's what they do in those motivational channel types
 
You don't have to share exactly what you're doing, but can you share some of the research papers you read for fingerprinting methods?
 
You can DM for any automated Audio / Video Manipulation. I can do it programmatically
 
that would be such a game changer knowing how to bypass audio id, I guess that's what they do in those motivational channel types

Yes, especially if we can use tools similar to what they use. It won't be as robust as what they use, but a 30% success rate is still better than 0%.

You don't have to share exactly what you're doing, but can you share some of the research papers you read for fingerprinting methods?

No problem bro, the research paper that I mentioned were these 2:

If you are familiar with Shazam, they use the same principal, and they already published a white paper on this:

https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf

The second paper was about the robustness of fingerprinting systems.
http://ismir2002.ircam.fr/proceedings/02-FP04-2.pdf

The tools that I used for fingerprinting is based on this blog post, the github repo is also included there.
https://willdrevo.com/fingerprinting-and-audio-recognition-with-python/

You can DM for any automated Audio / Video Manipulation. I can do it programmatically

How can you really help, you have some experience in AI?
Because I already have the tool but it needs something is off based on my testings, 30% success rate still needs a lot of improvements.
 
Yes, especially if we can use tools similar to what they use. It won't be as robust as what they use, but a 30% success rate is still better than 0%.



No problem bro, the research paper that I mentioned were these 2:

If you are familiar with Shazam, they use the same principal, and they already published a white paper on this:

https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf

The second paper was about the robustness of fingerprinting systems.
http://ismir2002.ircam.fr/proceedings/02-FP04-2.pdf

The tools that I used for fingerprinting is based on this blog post, the github repo is also included there.
https://willdrevo.com/fingerprinting-and-audio-recognition-with-python/



How can you really help, you have some experience in AI?
Because I already have the tool but it needs something is off based on my testings, 30% success rate still needs a lot of improvements.
Well, I can basically do anything with audio/video related. AI + Media editing Libraries. Just tell me what kind of manipulation you need and we can test it
 
In the past few months, my main focus has been on YouTube's Content ID system. While I believe I may have found a solution, it is not yet solid-proof. However, those who are knowledgeable about AI may be able to help us with the final piece of the puzzle.

We know that YouTube scans content and generates fingerprints for both the audio and video. These fingerprints are then stored in their database to compare against future duplicates. I am aware that creators often work with DRM companies, which act as intermediaries between creators and YouTube's Content ID system. These companies upload the creators' content and check for duplicates. If they find any, they share the revenue collected.

I have read some research papers on fingerprinting methods, which mention that the science still cannot detect some modifications when they are properly modified. I decided to investigate this further.

In my case, I only have problems with audio. Therefore, I concentrated on building my own fingerprinting tool to scan my modified audio sample against the original audio sample. Here are my results:

In these experiments I had two videos, which I will call X and Y.

1st Experiment, I used Video X:
I compared the original and modified audio samples using a fingerprinting technique. The results showed that the modified audio was 20% similar to the original. However, when I uploaded the video to a test channel, I received a claim when the video reached 100k views.

2nd Experiment, I used Video Y:
I then tested video Y with another layer of modifications on the audio, reducing the similarity to only 3% compared to the original audio. However, once again, the DRM company claimed my video after it received more than 130k views.

3rd Experiment I used Video X:
In my third experiment, I used the same video & audio from 1st experiment but added an extra modification to it, BOOM the claim was not made!
But the weird thing is that the similarity percentage with the original audio was 25%. This puzzled me, as the video received more than 700k views, they should've claimed it already!

Quick summary:
They claimed me when I had 20% similarity.
They claimed me when I had 3% similarity.

They didn’t claim me when I had 25% similarity which is the highest I had!

I continued with these experiments with different videos, but to sum up, it is still a hit-and-miss situation (30% success rate).
If anyone can provide more insight, it would be greatly appreciated.

PS: I know some people may think this is a lot of work and may ask why I can't use 100% original content. I have other channels where I use 100% original content, but blackhat STILL ROCKS my friend.

Your custom fingerprint tool and the one used by G are not the same, hence the difference in the numbers and why higher similarity was not detected, There could be other variables at play as well. your titles how popular the video/audio etc.

Although, I am no longer doing this, and my knowledge about this is limited, But the manual takedowns was what demotivated me from this.

how do you go around the manual takedowns ? changing titles and everything about the video description, thumbnail would increase the chance of not being claimed to a certain degree but then it would also slow down your views, since it would mean just another random video in the ocean.
 
Well, I can basically do anything with audio/video related. AI + Media editing Libraries. Just tell me what kind of manipulation you need and we can test it

I will see if I failed finding a solution, I will let you know.


Your custom fingerprint tool and the one used by G are not the same, hence the difference in the numbers and why higher similarity was not detected, There could be other variables at play as well. your titles how popular the video/audio etc.

Although, I am no longer doing this, and my knowledge about this is limited, But the manual takedowns was what demotivated me from this.

how do you go around the manual takedowns ? changing titles and everything about the video description, thumbnail would increase the chance of not being claimed to a certain degree but then it would also slow down your views, since it would mean just another random video in the ocean.

Of course, Google has multiple data points and dozens of engineers, so they MUST have better tools than I do!
However, if I can achieve a 30% success rate on my own, with the help of the community we can still improve the success rate.
 
How much time it took you to develop own fingerprinting tool ?
 
could use a little help if anyone knows any new methods or techniques, @JKM3NN4
Thnx
 
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