Today, a prominent child safety organization, Thorn, in partnership with a leading cloud-based AI solutions provider, Hive, announced the release of an AI model designed to flag unknown CSAM at upload. It’s the earliest AI technology striving to expose unreported CSAM at scale.
A generative model uses the classifier as part of its training. If you generate a picture of pure random noise, then iteratively pick random noise that the classifier says “looks” more like csam, then you can effectively generate images that the classifier says it’s 100% certain is csam. Whether or not that looks anything like what a human would consider to be csam depends on other factors but it remains a possibility.
You are describing the way deepdream works, not the way modern Diffusion models work. It’s the difference between psychedelic dog faces and a highly adherent generative image of a German Sheppard.
I can’t imagine you’re going to get anything out of this model that actually looks like CSAM, unless there’s some sort of breakthrough in using these models for previously unrealized generative purposes.