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

Leveraging Generative Models for Covert Messaging: Challenges and Tradeoffs for "Dead-Drop" Deployments