this post was submitted on 23 Oct 2023
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568
This new data poisoning tool lets artists fight back against generative AI (www.technologyreview.com)
submitted 1 year ago by [email protected] to c/[email protected]
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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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[–] [email protected] 9 points 1 year ago (2 children)

I'm sure others know more about it than i do but I thought pixels were very simple things.

You're right, in that pixels are very simple things. However, you and I can't tell one pixel from another in an image, and at the scale of modern digital art (my girlfriend does hers at 300dpi), shifting a handful of pixels isn't going to make much of a visible difference to a person, but a LLM will notice them.

[–] [email protected] 2 points 1 year ago* (last edited 1 year ago)

LLM is the wrong term. That's Large Language Model. These are generative image models / text-to-image models.

Truthfully though, while it will be there when the image is trained, it won't 'notice' it unless you distort it significantly (enough for humans to notice as well). Otherwise it won't make much of a difference because these models are often trained on a compressed and downsized version of the image (in what's called latent space)

[–] [email protected] 2 points 1 year ago

An AI model will "notice them" but ignore them if trained on enough copies with them to learn that they're not significant.