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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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[-] MargotRobbie@lemmy.world 332 points 1 year ago

It's made by Ben Zhao? You mean the "anti AI plagerism" UChicago professor who illegally stole GPLv3 code from an open source program called DiffusionBee for his proprietary Glaze software (reddit link), and when pressed, only released the code for the "front end" while still being in violation of GPL?

The Glaze tool that promised to be invisible to the naked eyes, but contained obvious AI generated artifacts? The same Glaze that reddit defeated in like a day after release?

Don't take anything this grifter says seriously, I'm surprised he hasn't been suspended for academic integrity violation yet.

[-] ElectroVagrant@lemmy.world 51 points 1 year ago

Thanks for added background! I haven't been monitoring this area very closely so wasn't aware, but I'd have thought a publication that has been would then be more skeptical and at least mention some of this, particularly highlighting disputes over the efficacy of the Glaze software. Not to mention the others they talked to for the article.

Figures that in a space rife with grifters you'd have ones for each side.

[-] Zeth0s@lemmy.world 29 points 1 year ago* (last edited 1 year ago)

Don't worry, it is normal.

People don't understand AI. Probably all articles I have read on it by mainstream media were somehow wrong. It often feels like reading a political journalist discussing about quantum mechanics.

My rule of thumb is: always assume that the articles on AI are wrong. I know it isn't nice, but that's the sad reality. Society is not ready for AI because too few people understand AI. Even AI creators don't fully understand AI (this is why you often hear about "emergent abilities" of models, it means "we really didn't expect it and we don't understand how this happened")

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[-] p03locke@lemmy.dbzer0.com 31 points 1 year ago

who illegally stole GPLv3 code from an open source program called DiffusionBee for his proprietary Glaze software (reddit link), and when pressed, only released the code for the “front end” while still being in violation of GPL?

Oh, how I wish the FSF had more of their act together nowadays and were more like the EFF or ACLU.

[-] MargotRobbie@lemmy.world 28 points 1 year ago

You should check out the decompilation they did on Glaze too, apparently it's hard coded to throw out a fake error upon detecting being ran on an A100 as some sort of anti-adversarial training measure.

[-] vidarh@lemmy.stad.social 11 points 1 year ago

That's hilarious, given that if these tools become remotely popular the users of the tools will provide enough adversarial data for the training to overcome them all by itself, so there's little reason to anyone with access to A100's to bother trying - they'll either be a minor nuisance used a by a tiny number of people, or be self-defeating.

[-] Dadifer@lemmy.world 13 points 1 year ago

Thank you, Margot Robbie! I'm a big fan!

[-] MargotRobbie@lemmy.world 18 points 1 year ago

You're welcome. Bet you didn't know that I'm pretty good at tech too.

Also, that's Academy Award nominated character actress Margot Robbie to you!

[-] lloram239@feddit.de 97 points 1 year ago

"New snake oil to give artists a false sense of security" - The last of these tools I tried had absolutely zero effect on the AI, which is not exactly surprising given that there are hundreds of different ways to make use of image data as well as lots of completely different models. You'll never cover that all with some pixel twisting.

[-] Blaster_M@lemmy.world 65 points 1 year ago

Oh no, another complicated way to jpeg an image that an ai training program will be able to just detect and discard in a week's time.

[-] vidarh@lemmy.stad.social 18 points 1 year ago

They don't even need to detect them - once they are common enough in training datasets the training process will "just" learn that the noise they introduce are not features relevant to the desired output. If there are enough images like that it might eventually generate images with the same features.

[-] egeres@lemmy.world 45 points 1 year ago

Here's the paper: https://arxiv.org/pdf/2302.04222.pdf

I find it very interesting that someone went in this direction to try to find a way to mitigate plagiarism. This is very akin to adversarial attacks in neural networks (you can read more in this short review https://arxiv.org/pdf/2303.06032.pdf)

I saw some comments saying that you could just build an AI that detects poisoned images, but that wouldn't be feasible with a simple NN classifier or feature-based approaches. This technique changes the artist style itself to something the AI would see differently in the latent space, yet, visually perceived as the same image. So if you're changing to a different style the AI has learned, it's fair to assume it will be realistic and coherent. Although maaaaaaaybe you could detect poisoned images with some dark magic tho, get the targeted AI then analyze the latent space to see if the image has been tampered with

On the other hand, I think if you build more robust features and just scale the data this problems might go away with more regularization in the network. Plus, it assumes you have the target of one AI generation tool, there are a dozen of these, and if someone trains with a few more images in a cluster, that's it, you shifted the features and the poisoned images are invalid

[-] vidarh@lemmy.stad.social 31 points 1 year ago

Trying to detect poisoned images is the wrong approach. Include them in the training set and the training process itself will eventually correct for it.

I think if you build more robust features

Diffusion approaches etc. do not involve any conscious "building" of features in the first place. The features are trained by training the net to match images with text features correctly, and then "just" repeatedly predict how to denoise an image to get closer to a match with the text features. If the input includes poisoned images, so what? It's no different than e.g. compression artifacts, or noise.

These tools all try to counter models trained without images using them in the training set with at most fine-tuning, but all they show is that models trained without having seen many images using that particular tool will struggle.

But in reality, the massive problem with this is that we'd expect any such tool that becomes widespread to be self-defeating, in that they become a source for images that will work their way into the models at a sufficient volume that the model will learn them. In doing so they will make the models more robust against noise and artifacts, and so make the job harder for the next generation of these tools.

In other words, these tools basically act like a manual adversarial training source, and in the long run the main benefit coming out of them will be that they'll prod and probe at failure modes of the models and help remove them.

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[-] nandeEbisu@lemmy.world 11 points 1 year ago

Haven't read the paper so not sure about the specifics, but if it relies on subtle changes, would rounding color values or down sampling the image blur that noise away?

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Lol... I just read the paper, and Dr Zhao actually just wrote a research paper on why it's actually legally OK to use images to train AI. Hear me out...

He changes the 'style' of input images to corrupt the ability of image generators to mimic them, and even shows that the super majority of artists even can't tell when this happens with his program, Glaze... Style is explicitly not copywriteable in US case law, and so he just provided evidence that the data OpenAI and others use to generate images is transformative which would legally mean that it falls under fair use.

No idea if this would actually get argued in court, but it certainly doesn't support the idea that these image generators are stealing actual artwork.

[-] Flambo@lemmy.world 13 points 1 year ago* (last edited 1 year ago)

So tl;dr he/his team did two things:

  1. argue the way AI uses content to train is legal
  2. provide artists a tool to prevent their content being used to train AI without their permission

On the surface it sounds all good, but I can't help but notice a future conflict of interest for Zhao should Glaze ever become monetized. If it were to be ruled illegal to train AI on content without permission, tools like Glaze would be essentially anti-theft devices, but while it remains legal to train AI this way, tools like Glaze stand to perhaps become necessary for artists to maintain the pre-AI status quo w/r/t how their work can be used and monetized.

[-] leaky_shower_thought@feddit.nl 30 points 1 year ago

I am sure we already got a budget version of this called the jpeg.

[-] seaQueue@lemmy.world 14 points 1 year ago

Speaking of jpeg I miss the "needs more jpeg" bot that used to run on reddit, that shit was hilarious.

[-] gregorum@lemm.ee 11 points 1 year ago

Reddit was Reddit for 18 fucking years. Just abandoning it leaves a massive hole. It’s gonna take a long time to fill it.

:(

[-] HappycamperNZ@lemmy.world 9 points 1 year ago

It really will.

Saying that, fuck spez

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[-] Vodik_VDK@lemmy.world 22 points 1 year ago

New CAPCHA just dropped.

[-] wizardbeard@lemmy.dbzer0.com 20 points 1 year ago

This is already a concept in the AI world and is often used while a model is being trained specifically to make it better. I believe it's called adversarial training or something like that.

[-] Mango@lemmy.world 13 points 1 year ago

No, that's something else entirely. Adversarial training is where you put an ai against a detector AI as a kind of competition for results.

[-] driving_crooner@lemmy.eco.br 8 points 1 year ago

Its called adversarial attack, this is an old video (5 years) explaining how it works and how you can potentially do it charging just one pixel on the image.

https://youtu.be/SA4YEAWVpbk?si=xObPveXTT2ip5ICG

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[-] Kolanaki@yiffit.net 19 points 1 year ago

"I can tell this is toxic by the pixels."

[-] ElectroVagrant@lemmy.world 11 points 1 year ago

"We like to call them poison pixels."

[-] uriel238@lemmy.blahaj.zone 15 points 1 year ago

I remember in the early 2010s reading an article like this one on openai.com talking about the dangers of using AI for image search engines to moderate against unwanted content. At the time the concern was CSAM salted to prevent its detection (along with other content salted with CSAM to generate false positives).

My guess is since we're still training AI with pools of data-entry people who tag pictures with what they appear to be, so that AI reads more into images than their human trainers (the proverbial man inside the Iron Turk).

This is going to be an interesting technology war.

[-] gregorum@lemm.ee 15 points 1 year ago

Ooo, this is fascinating. It reminds me of that weird face paint that bugs out facial-recognition in CCTV cameras.

[-] seaQueue@lemmy.world 8 points 1 year ago* (last edited 1 year ago)

Or the patterned vinyl wraps they used on test cars that interferes with camera autofocus.

[-] afraid_of_zombies@lemmy.world 14 points 1 year ago

I am waiting for the day that some obsessed person starts finding ways to do like code injection in pictures.

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[-] guyrocket@kbin.social 10 points 1 year ago

Invisible changes to pixels sound like pure BS to me. I'm sure others know more about it than i do but I thought pixels were very simple things.

[-] seaQueue@lemmy.world 28 points 1 year ago* (last edited 1 year ago)

"Invisible changes to pixels" means "a human can't tell the difference with a casual glance" - you can still embed a shit-ton of data in an image that doesn't look visually like it's been changed without careful inspection of the original and the new image.

If this data is added in certain patterns it will cause ML models trained against the image to draw incorrect conclusions. It's a technical hurdle that will slow a casual adversary, someone will post a model trained to remove this sometime soon and then we'll have a good old software arms race and waste a shit ton of greenhouse emissions adding and removing noise and training ever more advanced models to add and remove it.

You can already intentionally poison images so that image recognition draws incorrect conclusions fairly easily, this is the same idea but designed to cripple ML model training.

[-] Unaware7013@kbin.social 9 points 1 year ago

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.

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[-] RVMWSN@lemmy.ml 10 points 1 year ago* (last edited 1 year ago)

I generally don't believe in intellectual property, I think it creates artificial scarcity and limits creativity. Of course the real tragedies in this field have to do with medicine and other serious business. But still, artists claiming ownership of their style of painting is fundamentally no different. Why can't I paint in your style? Do you really own it? Are you suggesting you didn't base your idea mostly on the work of others, and no one in turn can take your idea, be inspired by it and do with it as they please? Do my means have to be a pencil, why can't my means be a computer, why not an algorythm? Limitations, limitations, limitations. We need to reform our system and make the public domain the standard for ideas (in all their forms). Society doesn't treat artists properly, I am well aware of that. Generally creative minds are often troubled because they fall outside norms. There are many tragic examples. Also money-wise many artists don't get enough credit for their contributions to society, but making every idea a restricted area is not the solution. People should support the artists they like on a voluntary basis. Pirate the album but go to concerts, pirate the artwork but donate to the artist. And if that doesn't make you enough money, that's very unfortunate. But make no mistake: that's how almost all artists live. Only the top 0.something% actually make enough money by selling their work, and that's is usually the percentile that's best at marketing their arts, in other words: it's usually the industry. The others already depend upon donations or other sources of income. We can surely keep art alive, while still removing all these artificial limitations, copying is, was and will never be in any way similar to stealing. Let freedom rule. Join your local pirate party.

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this post was submitted on 23 Oct 2023
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