Thanks for the response!
Ideally, F_Loss would be a measure of how well a classifier: F performs on the adversarial examples: x +G(x).
I want the generator: G to generate noise such that F_Loss is HIGH. This would indicate that the noise: G(x) when added to the image: x will cause F to make a misclassification.

I also want the generator: G to generate noise: G(x) such that the discriminator: D classifies G(x) + x as a real member of x. ie D(G(x) + x) == 1.
D on the other hand should classify G(x) + x as a fake image ie D(G(x)+x) == 0
The goal of this network is to train G to produce adversarial examples that look like real data, and fool F.