Martın Abadi and David G. Andersen recently published a paper on arxiv titled: "LEARNING TO PROTECT COMMUNICATIONS WITH ADVERSARIAL NEURAL CRYPTOGRAPHY" (https://arxiv.org/pdf/1610.06918v1.pdf).
I was wondering why Eve neural network $E$ does not get as input the learned parameters $\theta_A$ and $\theta_B$?
If really $K$ is supposed to be the only value that comprises the symmetric key, then in order to adhere to Kerckhoff's principle, one should view the training output (namely, the final values for $\theta_A$ and $\theta_B$) as public information. So, upon "alternating" the training phase to $E$, I supposed $E$ should receive as input $\theta_A$ and $\theta_B$ as well as $C$.
An alternative view would be that the training phase is regarded as a key generation algorithm. In such a view, the only challenge remaining is to really make training very efficient. But I do not think that this view was the intention of the authors because they explicitly contrasted their work to prior work that aims at generating cryptographic keys.
So my initial question still holds. Why is Kerckhoffs's principle (https://en.wikipedia.org/wiki/Kerckhoffs%27s_principle) apparently totally overlooked in this notion of neural cryptography? What am I getting wrong?