  State of the art generative models of human-produced content are the focus of
many recent papers that explore their use for steganographic communication. In
particular, generative models of natural language text. Loosely, these works
(invertibly) encode message-carrying bits into a sequence of samples from the
model, ultimately yielding a plausible natural language covertext. By focusing
on this narrow steganographic piece, prior work has largely ignored the
significant algorithmic challenges, and performance-security tradeoffs, that
arise when one actually tries to build a messaging pipeline around it. We make
these challenges concrete, by considering the natural application of such a
pipeline: namely, "dead-drop" covert messaging over large, public internet
platforms (e.g. social media sites). We explicate the challenges and describe
approaches to overcome them, surfacing in the process important performance and
security tradeoffs that must be carefully tuned. We implement a system around
this model-based format-transforming encryption pipeline, and give an empirical
analysis of its performance and (heuristic) security.
