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This new data poisoning tool lets artists fight back against generative AI (www.technologyreview.com)
submitted 2 years ago by to c/technology@lemmy.world
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A new tool lets artists add invisible changes to the pixels in their art before they upload it online so that if it’s scraped into an AI training set, it can cause the resulting model to break in chaotic and unpredictable ways.

The tool, called Nightshade, is intended as a way to fight back against AI companies that use artists’ work to train their models without the creator’s permission.
[...]
Zhao’s team also developed Glaze, a tool that allows artists to “mask” their own personal style to prevent it from being scraped by AI companies. It works in a similar way to Nightshade: by changing the pixels of images in subtle ways that are invisible to the human eye but manipulate machine-learning models to interpret the image as something different from what it actually shows.

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[–] -5 points 2 years ago* (1 child)

How is training AI with art on the web different to a person studying art styles?

Human brains clearly work differently than AI, how is this even a question?

The term "learning" in machine learning is mainly a metaphor.

Also, laws are written with a practical purpose in mind - they are not some universal, purely philosophical construct and never have been.

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  • [–] 9 points 2 years ago (1 child)

    Human brains clearly work differently than AI, how is this even a question?

    It's not all that clear that those differences are qualitatively meaningful, but that is irrelevant to the question they asked, so this is entirely a strawman.

    Why does the way AI vs. the brain learn make training AI with art make it different to a person studying art styles? Both learn to generalise features that allows them to reproduce them. Both can do so without copying specific source material.

    The term “learning” in machine learning is mainly a metaphor.

    How do the way they learn differ from how humans learn? They generalise. They form "world models" of how information relates. They extrapolate.

    Also, laws are written with a practical purpose in mind - they are not some universal, purely philosophical construct and never have been.

    This is the only uncontroversial part of your answer. The main reason why courts will treat human and AI actions different is simply that they are not human. It will for the foreseeable future have little to do whether the processes are similar enough to how humans do it.

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  • [–] -5 points 2 years ago* (last edited 2 years ago) (1 child)

    Now you're just cherry picking some surface-level similarities.

    You can see the difference in the process in the results, for example in how some generated pictures will contain something like a signature in the corner, simply because it resembles the training data - even though there is no meaning to it. Or how it is at least possible to get the model to output something extremely close to the training data - https://gizmodo.com/ai-art-generators-ai-copyright-stable-diffusion-1850060656.

    That at least proves that the process is quite different to the process of human learning.

    The question is how much those differences matter, and which similarities you want to focus on.

    Human learning is similar in some ways, but greatly differs in other ways.

    The fact that you're picking and choosing which similarities matter and which don't is just your arbitrary choice.

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  • [–] 9 points 2 years ago* (1 child)

    You can see the difference in the process in the results, for example in how some generated pictures will contain something like a signature in the corner

    If you were to train human children on an endless series of pictures with signatures in the corner, do you seriously think they'd not emulate signatures in the corner?

    If you think that, you haven't seen many children's drawings, because children also often pick up that it's normal to put something in the corner, despite the fact that to children pictures with signatures is a tiny proportion of visual input.

    Or how it is at least possible to get the model to output something extremely close to the training data

    People also mimic. We often explicitly learn to mimic - e.g. I have my sons art folder right here, full of examples of him being explicitly taught to make direct copies as a means to learn technique.

    We just don't have very good memory. This is an argument for a difference in ability to retain and reproduce inputs, not an argument for a difference in methods.

    And again, this is a strawman. It doesn't even begin to try to answer the questions I asked, or the one raised by the person you first responded to.

    That at least proves that the process is quite different to the process of human learning.

    Neither of those really suggests that all (that diffusion is different to humans learn to generalize images is likely true, what you've described does not provide even the start of any evidence of that), but again that is a strawman.

    There was no claim they work the same. The question raised was how the way they're trained is different from how a human learns styles.

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