Generating Steganographic Text With Lstms
Tina Fang, Martin Jaggi, Katerina Argyraki . Proceedings of ACL 2017, Student Research Workshop 2017 – 110 citations
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.