Comment on Astronomers discover technique to spot AI fakes using galaxy-measurement tools
elfpie@beehaw.org 4 months agoDoes it really work like that? I would say that they are not trying to fool any test, just getting harder to be detected. The goal being looking completely realistic.
This is one of the basic techniques to spot AI fakes:
The “test” they’re trying to fool, is kind of the Turing test: whether humans can tell them apart.
Consistent illumination and shadows is a rabbit hole we really don’t want to hop into.
Outside of very obvious anomalies even a trained eye will have a hard time discerning what’s going.
Some are very easy to spot, like a shadow of a character, that’s missing a limb on the shadow, or has different placement or pose. Illumination or parallel surfaces where they vary in shadowing without a reason, is also a dead giveaway. But the mist damning evidence is having one scene, then a slightly different scene in a reflection.
There are reasons for human authors to do any of these on purpose, but unless that purpose is part of the work, they’re most likely AI mistakes.
Of course it’s kind of funny how there is already a large overlap between the best AI art, and the most senseless “modern art”.
It’s quite easy to trick people with untrained eyes… for one, they have no idea what “consistent illumination” and stuff means. And something being off doesn’t mean that an AI made that mistake because humans make mistakes, too – photographs don’t, but the general problem is not just about telling realistic stuff apart but also illustrations. You’re looking specifically for mistakes that AI is likely to make, but humans are practically never going to make. And yes humans get hands wrong all the time.
Here’s a good video about what to look for and what not.
Yes, my comment applied more to photorealistic AI images.
Illustrations are a different beast, where people have much more creative freedom… and that video is reasonably good at explaining that, but I find it falls short at some points:
As AI generators advance, all these differences are likely to disappear… by following this same criticisms to fix things.
AI image generators don’t “consult” source images to generate an output.
Well, you have an artist breaking things down for an audience understanding neither the technical nor artistic aspect…
Modern AI generators are increasingly good at generating text. They still struggle a bit
I mean… SDXL still struggles a lot. The only thing you can get it to spell reliably is probably “Hooters”. There’s the one or other lora which makes it not suck completely but it’s still nowhere near actually good at generating text, the training just isn’t there. And even with that in place things like signatures are probably going to be gibberish.
While a naive (and cheaper) approach to AI generation doesn’t use layers, there are generators which do use layers,
Unless you start off training by feeding the model 3d data (say, voxels) alongside 2d projections I don’t think it’s ever going to develop a proper understanding of these kinds of things. Or, differently put: Learning object permanence (of sorts, related) is a meta-cognitive abstraction step that just won’t happen with the type of topologies we know how to engineer. It’s probably like 90% on the way towards AGI, so to get a simple topology to understand it we have to spoon-feed it permanence information alongside the (apparent) non-permanence.
AndrasKrigare@beehaw.org 4 months ago
Looking completely realistic and being able to discern between real and fake are competing goals. If you can discern the difference, then it does not look completely realistic.
I think what they’re alluding to is generative adversarial networks …wikipedia.org/…/Generative_adversarial_network where creating a better discriminator that can detect a good image from bad is how you get a better image.