No, GANs train exactly one discriminator, jointly with the generator. There's no guarantee that you can't train another good discriminator out of band.

Furthermore, GAN discriminators are (as I understand it) often hobbled a bit to ensure that the generator can make progress on the loss function. An always-correct D doesn't provide a useful gradient.

GANs may produce imagery or audio which fools humans, but they are unlikely to consistently produce imagery or audio which fools humans over time.

GANs train by fooling AIs, not humans. Fooling humans is a side effect, not the primary thing trained for (mostly because that's cheaper of course). It's just that humans are in some ways different from AIs in terms of fooling.

Looking at the papers I must say I think the ability to fool humans is a scale problem, not a fundamental limitation. Already GAN produced images and sounds survive "normal" human scrutiny: if you have no reason to suspect foul play you won't see it. If you really go looking, you'll see it.

Snake oil-tier anti-ML might do the trick. One of the problems is the level of confidence people put into ML in the first, when a lot of times it's also snake oil-quality. Just being able to cast doubt on that again would help prevent a loss of healthy skepticism and critical thinking.