One of the tests you describe - a classifier trained to differentiate between machine and human generated art - is exactly what the ML GAN model is doing.
The generative adversarial network (GAN) has a generative part and a discriminative part which compete with each other. The generative model creates the images that you see in the gallery. The discriminative model tries to predicts whether an image is real or was generated. When it starts failing often, we know that the generative model is getting better (of course, the details are much more complicated).
The generative adversarial network (GAN) has a generative part and a discriminative part which compete with each other. The generative model creates the images that you see in the gallery. The discriminative model tries to predicts whether an image is real or was generated. When it starts failing often, we know that the generative model is getting better (of course, the details are much more complicated).