This is inherently a super hard problem, because (to oversimplify) the loss function of any AI generating NN is to minimize the difference between a human generated and AI generated images. So the state of the art for detection & generation is always going to be pretty close.

oh i see thanks for the reply!

Just because it can imitate doesn't mean it can do so perfectly.

To add to this, openai itself is pretty mediocre at detecting AI generated text: https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text/

The model is trained by getting rewarded for fooling a model that tries to distinguish between the real and fake images. So no, it won’t be perfect, but it’s going to be good enough to trick a model the vast majority of the time because that is literally a part of the training. Not just a small part, that’s is the central tenet of the training and optimization of generative models, generative ADVERSARIAL networks.

Your argument falls apart when you realize that there are training artifacts. Ever wonder why FID scales inversely with model size?

You’re correct that the state of the arts will be close but that doesn’t mean that detection cannot train and predict on generated data. It’s like using synthetic data.