The optimal modification of a two-state LLM-like token distribution for maximum steganographic capacity under a divergence budget is a deterministic, piecewise water-filling policy.
Generating Steganographic Text with LSTMs
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abstract
Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network. We demonstrate our approach on the Twitter and Enron email datasets and show that it yields high-quality steganographic text while significantly improving capacity (encrypted bits per word) relative to the state-of-the-art.
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Relatively-Secure LLM-Based Steganography via Constrained Markov Decision Processes
The optimal modification of a two-state LLM-like token distribution for maximum steganographic capacity under a divergence budget is a deterministic, piecewise water-filling policy.