Back in 2022, the tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI's DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados. But artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work. Ben Zhao, a computer security researcher at the University of Chicago, was listening. He and his colleagues have built arguably the most prominent weapons in an artist's arsenal against nonconsensual AI scraping: two tools called Glaze and Nightshade that add barely perceptible perturbations to an image's pixels so that machine-learning models cannot read them properly.

On the call, artists shared details of how they had been hurt by the generative AI boom, which was then brand new. At that moment, AI was suddenly everywhere. The tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI's DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados. But these artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work.

Now artists are fighting back. And some of the most powerful tools they have were built by Shawn Shan, 26, a PhD student in computer science at the University of Chicago (and MIT Technology Review's 2024 Innovator of the Year). Shan got his start in AI security and privacy as an undergraduate there and participated in a project that built Fawkes, a tool to protect faces from facial recognition technology. But it was conversations with artists who had been hurt by the generative AI boom that propelled him into the middle of one of the biggest fights in the field. Soon after learning about the impact on artists, Shan and his advisors Ben Zhao (who made our Innovators Under 35 list in 2006) and Heather Zheng (who was on the 2005 list) decided to build a tool to help. They gathered input from more than a thousand artists to learn what they needed and how they would use any protective technology.

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