Click For Photo: https://regmedia.co.uk/2018/08/08/listening_music.jpg
Analysis YouTube is understood to use machine-learning algorithms to identify copyrighted material in user-uploaded videos, so that, in theory at least, any artists featured are properly compensated for their work. This system works more or less, though it is not without its controversies.
Concerns over heavy-handedness and fair use rights aside, it turns out AI algorithms can, allegedly subtly, tweak the audio in video submissions so that any copyrighted music present can evade detection by YouTube's AI bots after they are uploaded.
Boffins - University - Maryland - America - Code
Boffins at University of Maryland in America reckon their code successfully manipulated the audio in two songs – Stevie Wonder’s smash hit Signed, Sealed, Delivered that peaked at number 3 in 1970; and Kesha’s infectious track Tik Tok, which topped the chart in 2010 – so that after they were uploaded to YouTube, they avoided detection, and still sounded more or less the same as the originals.
This doctored audio is a type of adversarial attack in which neural networks tweak an input, such as a photograph, to produce a slightly vandalized output that hoodwinks another neural network into misidentifying that input data. Adversarial attacks are most commonly performed on computer vision models, such as the carefully tweaked toy turtle that was subsequently mistaken by machine-learning software for a gun.
Audio - Systems - YouTube - Copyright - Detectives
And, yes, adversarial audio can be crafted, too, causing systems such as YouTube's digital copyright detectives to misidentify the music – as described by the Uni of Maryland team in this academic paper [PDF] shared online this week.
The adversarial audio derived from the aforementioned Stevie Wonder and Kesha’s songs were used to attack Youtube’s Content ID algorithm. Every time a video is uploaded to YouTube, it’s automatically scanned by Content ID, which checks to see if the footage and audio in the clip match any of the material in its database of...
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