REVIEW 1 cited by
Generating Steganographic Text with LSTMs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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.
Discussion (0). Continue with ORCID to comment.