Why is detecting AI-generated images so hard on real-world scenarios? And what seems to work with good generalization between models?
I've been working on creating an AI-generated image detector and everything so called "state-of-the-art" in academic studies failed when I tried on a real-world scenarios. State-of-art detectors suffer from bad generalization (the artifacts produced by newer generators differ from those on which the detectors were trained); in-the-wild disturbances such as hard jpeg compression and automatic image post-processing some smartphones have tend to attenuate ai-generated artifacts; overlapping distributions on almost all image statistcs between fake and real datasets, considering features used in digital forensics.
I'm really struggling to make anything relliable. For those who are currently developing ai-generated image detectors, what is working for you?