Nel caso ti servisse un esempio eclatante, Marco, quando parli di bolle giuridiche:

Extracting Training Data from Diffusion Models

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due
to their ability to generate high-quality synthetic images.
In this work, we show that diffusion models memorize individual images from their training data and emit them
at generation time. With a generate-and-filter pipeline, we extract over a thousand training examples from stateof-
the-art models, ranging from photographs of individual people to trademarked company logos. We also train
hundreds of diffusion models in various settings to analyze how different modeling and data decisions affect
privacy. Overall, our results show that diffusion models are much less private than prior generative models such
as GANs, and that mitigating these vulnerabilities may require new advances in privacy-preserving training.