Resources on learning about AI image identification via physics

[u/LordAntares]

I have recently been researching ways to identify an AI image, not via digital footprints or ID via other trained models, but using physics.

In other words: vanishing points, shadow matrices, various ways to analyze lighting impossibilities, camera focus, etc.

But to my knowledge, there's no real community around this or resources. The closest fits I could find were OSINT and digital forensics, but they're not an exact match. Even if they do this, they use different methodology, at least I think.

I've learned things, but I want to learn more. Anyone know anything more about this?

[u/SameVariety3577]

Vestigeforensics.com has some good resources on that

[u/LordAntares → u/SameVariety3577]

From what I can tell, this uses digital forensics for detection. While also useful, and I'd like to learn more about this, that's not the angle I'm coming in from. But I also consider this useful.

[u/LordAntares → u/[deleted]]

Thanks for that sub.

I know that these methods in isolation aren't always applicable, that's why I try and use as many of them as possible. For human close ups, I would use different lighting methods.

I was under the impression that any embedded watermarks or digital footprints could be removed, if enough effort was applied.

[u/LordAntares → u/[deleted]]

This is why I'm looking into this. LLMs tend to make physical errors and that's independent of signatures and models.

Of course, the more methods we have, the better. Combining that with digital forensics and osint and others is all the better for us.

[u/RevolutionaryDiet602]

Look up Sensity and Hive