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The AI software Stable Diffusion has a remarkable ability to turn text into images. When I asked the software to draw "Mickey Mouse in front of a McDonald's sign," for example, it generated the picture you see above.
Stable Diffusion can do this because it was trained on hundreds of millions of example images harvested from across the web. Some of these images were in the public domain or had been published under permissive licenses such as Creative Commons. Many others were not—and the world's artists and photographers aren't happy about it.
In January, three visual artists filed a class-action copyright lawsuit against Stability AI, the startup that created Stable Diffusion. In February, the image-licensing giant Getty filed a lawsuit of its own.
[...]
The plaintiffs in the class-action lawsuit describe Stable Diffusion as a "complex collage tool" that contains "compressed copies" of its training images. If this were true, the case would be a slam dunk for the plaintiffs.But experts say it's not true. Erik Wallace, a computer scientist at the University of California, Berkeley, told me in a phone interview that the lawsuit had "technical inaccuracies" and was "stretching the truth a lot." Wallace pointed out that Stable Diffusion is only a few gigabytes in size—far too small to contain compressed copies of all or even very many of its training images.
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(Score: 2) by ilsa on Tuesday April 04 2023, @04:31PM (3 children)
Agreed. If they try to push the case based on how the tech works, they lost before they even filed cause they dunno WTF they're talking about. The model doesn't contain any training data at all. It contains a construct that was generated from that training data.
If they do lose because of this, I hope it doesn't set a precedent that sets back future lawsuits that are more intelligently crafted.
The correct argument is that the training data uses data sources that they cannot reliably prove were public domain. Further, the system is not capable of unique results. It is only capable of producing an amalgam of what it was trained on, with no unique insights or variations. If the model was trained on one single picture of a woman, and you said "give me a picture of a woman", it will won't produce a picture of a generic woman, but that exact woman it was trained on, along with the clothing and the background of the image. It doesn't understand what a "woman" is, beyond the context that the image it trained on was tagged as "woman".
That means if they trained on copyrighted images, it is virtually guaranteed to reproduce those copyrighted images, which the Ars article demonstrates. Therefore, and again as Ars points out, the argument actually has nothing to do with AI per se, but of fair use.
(Score: 2, Touché) by Anonymous Coward on Tuesday April 04 2023, @05:59PM (2 children)
"My ZIP file doesn't contain any images at all! It contains a construct that was generated from the images!"
Since it has been shown that one can extract a replica of training images from this "construct", albeit with compression artefacts, how can it be claimed that's this "construct" doesn't contain the training data?
(Score: 3, Interesting) by ledow on Wednesday April 05 2023, @07:15AM (1 child)
It doesn't contain identical copies of the training data in the exact format provided, that much is likely true. The system doesn't "understand" JPEG or similar, and it likely isn't storing them like that at all.
But it doesn't need to in order to be copyright infringement.
Literally any "derivative work". And that's such a broad definition that it encompasses basically the entire "AI" training database and any resulting intermediary format and output whatsoever, if it's sufficiently close to the original data to be recognisable to a human (which is kind of the whole point, no?).
(Score: 0) by Anonymous Coward on Wednesday April 05 2023, @10:14AM
Yeah I mean we can already see some stuff here: https://youtu.be/Ok44otx90D4 [youtu.be]
See also: https://en.wikipedia.org/wiki/Rogers_v._Koons [wikipedia.org]
Where it doesn't even need to be a very close copy.