This is a good idea. I wonder if deep-learning will be useful to strongly test it this way: 1. divide a set of documents from the same person , apply stenograp

This is a good idea. I wonder if deep-learning will be useful to strongly test it this way: 1. divide a set of documents from the same person , apply stenography to half. represent each word by a number. 2. take half of this data and use it to train your deep-learning algorithm to categorize documents as clear/stegoe'd. Than test how well it can detect stego in the other half. The inputs to those deep-learning should be as followed: num_of_word_0, synonym_number(num_of_word_0), .., num_of_word_n, synonym_number(num_of_word_n) Where synonym_number(word) is: if we start with home-house-abode from wordnet as synonyms, home=1, abode=3. This way we give the deep-learner info about your synonym use stats. 3. run algorithm. Not too complex, and there's a decent likelihood that if there are statistical patterns, the deep-learner will find them.