For example, projects trying to detect artifacts in data generated by a neural network, using a “simple” algorithm. The same way compression can be seen when data is analyzed. Anything that isn’t “our neural network detects other neural networks” and that isn’t some proprietary bullshit.
Projects trying to block scrapers as best they can or feed them garbage data.
Some collaborative networks for detecting and storing in a database famous data like images or text which has very likely been generated by a neural network. Only if the methods of detection are explained and can be verified of course, otherwise anybody can claim anything.
It would be nice to have a updating pinned post or something with links to research or projects trying to untangle this mess.
The only project I can think of now: https://xeiaso.net/blog/2025/anubis/
For artists the first thing that came to mind was Nightshade: https://nightshade.cs.uchicago.edu/whatis.html
Yep that’s nice, although it seems to be proprietary which isn’t ideal, it’s the last thing we need now. Companies exploited the hell out of everything, now companies/universities exploiting the solutions too, when there’s absolutely nothing stopping it from being open.
It’s interesting but can’t work outside of a lab. The example they gave was watermarking a picture of a cow with a purse. If every cow picture has a hidden purse watermark, an AI will be trained into categorizing a cow as a purse.
But to make that work in the real world would require every artist and especially stock photo sites to agree to watermark every cow with a purse. If everyone doesn’t pick a consistent watermark of a purse for a cow, then it becomes noise that is trained out. Just like training a to identify a cow sometimes has a farmhouse in the picture, other times grass, other times birds. It learns cow because that’s the consistent part. Without a purse watermarked into the majority of every cow photo everywhere, the ai will learn cow.