Submission + - SPAM: AI models spit out photos of real people and copyrighted images
guest reader writes: Popular image generation models can be prompted to produce identifiable photos of real people, potentially threatening their privacy, according to new research. The work also shows that these AI systems can be made to regurgitate exact copies of medical images and copyrighted work by artists. It's a finding that could strengthen the case for artists who are currently suing AI companies for copyright violations.
The researchers, from Google, DeepMind, UC Berkeley, ETH Zürich, and Princeton, got their results by prompting Stable Diffusion and Google's Imagen with captions for images, such as a person's name, many times. Then they analyzed whether any of the images they generated matched original images in the model's database. The group managed to extract over 100 replicas of images in the AI's training set.
The paper with title "Extracting Training Data from Diffusion Models" is the first time researchers have managed to prove that these AI models memorize images in their training sets, says Ryan Webster, a PhD student at the University of Caen Normandy in France.
For example, recent class-action lawsuit accusing DeviantArt, Midjourney and Stability AI uses the following arguments as a claim:
The resulting image is necessarily a derivative work, because it is generated exclusively from a combination of the conditioning data and the latent images, all of which are copies of copyrighted images. It is, in short, a 21st-century collage tool.
A diffusion model is a form of lossy compression applied to the Training Images. Because a trained diffusion model can produce a copy of any of its Training Images—which could number in the billions—the diffusion model can be considered an alternative way of storing a copy of those images. In essence, it's similar to having a directory on your computer of billions of JPEG image files. But the diffusion model uses statistical and mathematical methods to store these images in an even more efficient and compressed manner.
A diffusion model is then able to reconstruct copies of each Training Image. Furthermore, being able to reconstruct copies of the Training Images is not an incidental side effect. The primary goal of a diffusion model is to reconstruct copies of the training data with maximum accuracy and fidelity to the Training Image. It is meant to be a duplicate.
There are a number of laws that protect and preserve the rights and interests with respect to their art. Provided references are 17 U.S.C. 106 and Section 1202(c) of the DMCA.
Link to Original Source
The researchers, from Google, DeepMind, UC Berkeley, ETH Zürich, and Princeton, got their results by prompting Stable Diffusion and Google's Imagen with captions for images, such as a person's name, many times. Then they analyzed whether any of the images they generated matched original images in the model's database. The group managed to extract over 100 replicas of images in the AI's training set.
The paper with title "Extracting Training Data from Diffusion Models" is the first time researchers have managed to prove that these AI models memorize images in their training sets, says Ryan Webster, a PhD student at the University of Caen Normandy in France.
For example, recent class-action lawsuit accusing DeviantArt, Midjourney and Stability AI uses the following arguments as a claim:
The resulting image is necessarily a derivative work, because it is generated exclusively from a combination of the conditioning data and the latent images, all of which are copies of copyrighted images. It is, in short, a 21st-century collage tool.
A diffusion model is a form of lossy compression applied to the Training Images. Because a trained diffusion model can produce a copy of any of its Training Images—which could number in the billions—the diffusion model can be considered an alternative way of storing a copy of those images. In essence, it's similar to having a directory on your computer of billions of JPEG image files. But the diffusion model uses statistical and mathematical methods to store these images in an even more efficient and compressed manner.
A diffusion model is then able to reconstruct copies of each Training Image. Furthermore, being able to reconstruct copies of the Training Images is not an incidental side effect. The primary goal of a diffusion model is to reconstruct copies of the training data with maximum accuracy and fidelity to the Training Image. It is meant to be a duplicate.
There are a number of laws that protect and preserve the rights and interests with respect to their art. Provided references are 17 U.S.C. 106 and Section 1202(c) of the DMCA.
Link to Original Source
