levand t1_j7o5zeb wrote on February 8, 2023 at 5:03 AM

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.

Such_Share8197 OP t1_j7o6crv wrote on February 8, 2023 at 5:06 AM

oh i see thanks for the reply!

thelibrarian101 t1_j7p27ns wrote on February 8, 2023 at 11:53 AM

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/

enterthesun t1_j7uqcx7 wrote on February 9, 2023 at 3:32 PM

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.

nutpeabutter t1_j7os2xj wrote on February 8, 2023 at 9:36 AM

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

DMLearn t1_j7pq8wc wrote on February 8, 2023 at 3:16 PM

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.

nutpeabutter t1_j7rxvb8 wrote on February 8, 2023 at 11:48 PM

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