[leod]

Interesting. They train an image classifier to detect images that were generated by a GAN-trained CNN. I wonder if it could be possible to include this classifier in the training loss, such that the generated images fly under its radar as much as possible. If this makes sense, then I guess the cat-and-mouse game just gained another level. On the other hand, what the classifier is detecting could be a fingerprint of the CNN architecture itself.

(Full disclosure: I have only read the abstract so far.)

[NoodleIncident]

> Due to the difficulties in achieving Nash equilibria, none of the current GAN-based architectures are optimized to convergence, i.e. the generator never wins against the discriminator.

If I understand the terms used, it sounds like you're suggesting adding this classifier to the discriminator, to avoid detection. Since they are already failing to pass their existing discriminators, it seems like they could try to not be detected, but they wouldn't actually succeed.