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Slashdot: A Novel Data Poisoning Tool Empowers Artists to Counteract Generative AI


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    An anonymous reader quotes a report from MIT Technology Review: 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. Using it to "poison" this training data could damage future iterations of image-generating AI models, such as DALL-E, Midjourney, and Stable Diffusion, by rendering some of their outputs useless -- dogs become cats, cars become cows, and so forth. MIT Technology Review got an exclusive preview of the research, which has been submitted for peer review at computer security conference Usenix.

    AI companies such as OpenAI, Meta, Google, and Stability AI are facing a slew of lawsuits from artists who claim that their copyrighted material and personal information was scraped without consent or compensation. Ben Zhao, a professor at the University of Chicago, who led the team that created Nightshade, says the hope is that it will help tip the power balance back from AI companies towards artists, by creating a powerful deterrent against disrespecting artists' copyright and intellectual property.

    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. The team intends to integrate Nightshade into Glaze, and artists can choose whether they want to use the data-poisoning tool or not. The team is also making Nightshade open source, which would allow others to tinker with it and make their own versions. The more people use it and make their own versions of it, the more powerful the tool becomes, Zhao says. The data sets for large AI models can consist of billions of images, so the more poisoned images can be scraped into the model, the more damage the technique will cause.

    Nightshade exploits a security vulnerability in generative AI models, one arising from the fact that they are trained on vast amounts of data -- in this case, images that have been hoovered from the internet. Nightshade messes with those images. Artists who want to upload their work online but don't want their images to be scraped by AI companies can upload them to Glaze and choose to mask it with an art style different from theirs. They can then also opt to use Nightshade. Once AI developers scrape the internet to get more data to tweak an existing AI model or build a new one, these poisoned samples make their way into the model's data set and cause it to malfunction. Poisoned data samples can manipulate models into learning, for example, that images of hats are cakes, and images of handbags are toasters. The poisoned data is very difficult to remove, as it requires tech companies to painstakingly find and delete each corrupted sample.

    Nightshade is going to make AI companies think twice, "because they have the possibility of destroying their entire model by taking our work without our consent," says Eva Toorenent, an illustrator and artist who has used Glaze.

    "I'm just really grateful that we have a tool that can help return the power back to the artists for their own work," added Autumn Beverly, another artist.

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