Generating Steganographic Text with LSTMs

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.Comment: ACL 2017 Student Research Worksho

Detecting Steganography

The aim of this project is to assess the non- detectability of slightly modified version of the linguistic steganography approach described in the paper Generating Steganographic Text with LSTMs by classifying whether a tweet was generated by a regular user or by this stegano- graphic method. For that purpose, both a fastText supervised classification and a user study are conducted