How long will this work?
It's an arms race the bigger players will win, and it undermines the quality of the images. But it feels natural that artists would want to do something since they don't feel like anyone else is protecting them right now.
Nightshade: An offensive tool for artists against AI art generators
41–50 of 710 posts
Re: Nightshade: An offensive tool for artists against AI art generators
#42Earlier quoted context omitted.
I did not say they were exclusively that; I said they were that. Once we as a society have implemented a good way for the artists whose work powers these machines to survive, you can feel good about using them. Until then, frankly, you're doing something immoral by paying to use them.
What if I run Stable Diffusion locally without paying anyone anything? Is it less immoral?
Marginally, yeah, since you're not supporting the development of more capable labor-saving devices in this category.
I'm still not a fan, though.
Re: Nightshade: An offensive tool for artists against AI art generators
#43Remember when the music industry tried to use technology to stop music pirating?
This will work about as well...
Oh, I forget, fighting music pirating was considered an evil thing to do on HN. "pirating is not stealing, is copyright infringement", right? Unlike training neural nets on internet content which of course is "stealing".
Re: Nightshade: An offensive tool for artists against AI art generators
#44>Like Glaze, Nightshade is computed as a multi-objective optimization that minimizes visible changes to the original image. It's still noticeably visible.
Yeah, I've seen multiple artists complain about how glazing reduces image quality. It's very noticeable. That seems like an unavoidable problem given how AI is trained on images right now.
Re: Nightshade: An offensive tool for artists against AI art generators
#45Earlier quoted context omitted.
> they should license the content they use for the training data You mean like OpenAI and Adobe ? Only the free and open source models didn't licensed any content for the training data.
Adobe is training off of images stored in their cloud systems, per their Terms of Service. OpenAI has provided no such documentation or legal guarantees, and it is still quite possible they scraped all sorts of copyright materials.
Google scrapes copyrighted material every day and then presents that material to users in the form of excerpts, images, and entire book pages. This has been ruled OK by the courts. Scraping copyrighted information is not illegal or we couldn't have search engines.
Re: Nightshade: An offensive tool for artists against AI art generators
#46Earlier quoted context omitted.
Obviously AIs can just train on images that aren't poisoned.
Is it possible to reliably detect whether an image is poisoned? If not then it achieves the goal of punishing entities which indiscriminately harvest data.
It's roughly in the same spot as reliably detecting if you have permission to use the image for your data training set in the first place.
If it doesn't matter, then neither does the poisoning matter.
Re: Nightshade: An offensive tool for artists against AI art generators
#47Earlier quoted context omitted.
It's going to be interesting to see how the lawsuits against OpenAI by content creators plays out. If the courts rule that AI generated content is a derivative work of all the content it was trained on it could really flip the entire gen AI movement on its head.
If it were a derivative work[1] (and sufficiently transformational) then it's allowed under current copyright law and might not be the slam dunk ruling you were hoping for. [1] https://en.wikipedia.org/wiki/Derivative_work
"sufficiently transformational" is carrying a lot of water here. At minimum it would cloud the issue and might expose anyone using AI to lawsuits where they'd potentially have to defend each generated image.
Re: Nightshade: An offensive tool for artists against AI art generators
#48This seems to introduce levels of artifacts that many artists would find unacceptable: https://twitter.com/sini4ka111/status/1748378223291912567
The rumblings I'm hearing are that this a) barely works with last-gen training processes b) does not work at all with more modern training processes (GPT-4V, LLaVA, even BLIP2 labelling [1]) and c) would not be especially challenging to mitigate against even should it become more effective and popular. The Authors' previous work, Glaze, also does not seem to be very effective despite dramatic proclamations to the contrary, so I think this might be a case of overhyping an academically interesting but real-world-impractical result.
[1]: Courtesy of /u/b3sn0w on Reddit: https://imgur.com/cI7RLAq https://imgur.com/eqe3Dyn https://imgur.com/1BMASL4
Re: Nightshade: An offensive tool for artists against AI art generators
#49Re: Nightshade: An offensive tool for artists against AI art generators
#50Earlier quoted context omitted.
Obviously AIs can just train on images that aren't poisoned.
Is it possible to reliably detect whether an image is poisoned? If not then it achieves the goal of punishing entities which indiscriminately harvest data.
You can use older images, collected from before the "poisoning" software was released. Then you don't have to.
This, of course, assumes that "poisoning" actually works. Glaze and Nightshade and similar are very much akin to the various documented attacks on facial recognition systems. The attack does not exploit some fundamental flaw in how the systems work, but specific characteristics in a given implementation and version.
This matters because it means that later versions and models will inevitably not have the same vulnerabilities. The result is that any given defensive transformation should be expected to be only narrowly effective.