Surely if you want you can train the network to produce images that are not easily detectable?
So:
1) train a network that can detect CNN generated images
2) train the CNN network to generate whatever you want, politicians in compromising positions, etc. but also add in weights against the the other network
3) Images won't be easy to spot...
People will obviously start writing CNNs that detect images that are generated obfuscated this way with CNNs, but still, it's all possible.
What you describe is exactly the way these models work!
Typically; a GAN (Generative Adversarial Network) consists of (1) the generator; a model generating images and (2) the discriminator; a model that learns whether images it is fed come from the generator or from the image dataset. The (gradient) information of how the discriminator made its decision is fed back into the generator, in order to help it learn how to generate more _real_ images.
The discriminator is what you describe in step 1, and the generator is your step 2.
So:
1) train a network that can detect CNN generated images
2) train the CNN network to generate whatever you want, politicians in compromising positions, etc. but also add in weights against the the other network
3) Images won't be easy to spot...
People will obviously start writing CNNs that detect images that are generated obfuscated this way with CNNs, but still, it's all possible.