A.I. News University of Chicago researchers seek to “poison” AI art generators with Nightshade


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Aug 17, 2017
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. Meta, Google, Stability AI, and OpenAI did not respond to request for comment on how they might respond.

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On Friday, a team of researchers at the University of Chicago released a research paper outlining "Nightshade," a data poisoning technique aimed at disrupting the training process for AI models, reports MIT Technology Review and VentureBeat. The goal is to help visual artists and publishers protect their work from being used to train generative AI image synthesis models, such as Midjourney, DALL-E 3, and Stable Diffusion.

The open source "poison pill" tool (as the University of Chicago's press department calls it) alters images in ways invisible to the human eye that can corrupt an AI model's training process. Many image synthesis models, with notable exceptions of those from Adobe and Getty Images, largely use data sets of images scraped from the web without artist permission, which includes copyrighted material. (OpenAI licenses some of its DALL-E training images from Shutterstock.)

AI researchers' reliance on commandeered data scraped from the web, which is seen as ethically fraught by many, has also been key to the recent explosion in generative AI capability. It took an entire Internet of images with annotations (through captions, alt text, and metadata) created by millions of people to create a data set with enough variety to create Stable Diffusion, for example. It would be impractical to hire people to annotate hundreds of millions of images from the standpoint of both cost and time. Those with access to existing large image databases (such as Getty and Shutterstock) are at an advantage when using licensed training data.


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