A Synonym-Substitution Based Algorithm for Text Steganography (2012) [pdf]

Laughed out loud reading that title. So clever.

I once implemented a steganography system to manipulate the ordering of class files in a java jar file (they retain order therein).

The "distance from alphabetically sorted" was calculate-able as a BigInteger. That BigInteger was encode-able to a text message. Hence, a text message could be hidden in the ordering of the class files (assuming there were some reasonable number of class files - and there usually is)

I kept wondering if the author applied any of the text/image steganographic techniques discussed to the paper itself, in a self referential sort of way. Had I been the author, I'd have felt obligated to do so.

From the document:

> One proposed solution by Judge [14] uses spelling errors to hide data, for example spelling ”is” as ”iz”. A correctly spelled word indicated a zero, a incorrectly spelled word a 1.

And one can see some spelling mistakes or variations across the document... (For instance:"This attack involves the analysis of know patterns the correspond to hidden information" or "being able to here the data hidden in audio" )

I have access to some of Apple's secret documentation and they watermark your name and email address over all the PDFs. I often wondered if they do something like this as well, just in case.

Only using synonyms is pretty limited though. You could go much further:

1. In PDF documents use layouts that differ by only a couple of pixels. 2. Change the placement of figures. 3. Change brackets to dashes (like this) - or like this. 4. Add double spaces in places. 5. Adjust the hyphenation at the end of long lines. 6. Use unicode look-a-like characters. 7. Use different unicode variations of dashes and quotation marks.

If you only had a single document and wanted to leak it while being certain that your name wouldn't be leaked too, the only way I can see to do it would be to rewrite it in your own words.

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.

It seems like this would be detectable using a technique talked about by Bruce Schneier in 2011 (https://www.schneier.com/blog/archives/2011/08/identifying_p...) called forensic linguistics (https://en.wikipedia.org/wiki/Forensic_linguistics)

I've wondered about doing this before for copy protection; mixing the order of bullet points, and so on. You can't stop copies, but you can see who made the copies potentially.

This might also be useful for combating attempts to reveal authors' identities using forensic linguistics.