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Proceedings Paper

Linguistic steganography on Twitter: hierarchical language modeling with manual interaction
Author(s): Alex Wilson; Phil Blunsom; Andrew D. Ker
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Paper Abstract

This work proposes a natural language stegosystem for Twitter, modifying tweets as they are written to hide 4 bits of payload per tweet, which is a greater payload than previous systems have achieved. The system, CoverTweet, includes novel components, as well as some already developed in the literature. We believe that the task of transforming covers during embedding is equivalent to unilingual machine translation (paraphrasing), and we use this equivalence to de ne a distortion measure based on statistical machine translation methods. The system incorporates this measure of distortion to rank possible tweet paraphrases, using a hierarchical language model; we use human interaction as a second distortion measure to pick the best. The hierarchical language model is designed to model the speci c language of the covers, which in this setting is the language of the Twitter user who is embedding. This is a change from previous work, where general-purpose language models have been used. We evaluate our system by testing the output against human judges, and show that humans are unable to distinguish stego tweets from cover tweets any better than random guessing.

Paper Details

Date Published: 19 February 2014
PDF: 17 pages
Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 902803 (19 February 2014); doi: 10.1117/12.2039213
Show Author Affiliations
Alex Wilson, Univ. of Oxford (United Kingdom)
Phil Blunsom, Univ. of Oxford (United Kingdom)
Andrew D. Ker, Univ. of Oxford (United Kingdom)

Published in SPIE Proceedings Vol. 9028:
Media Watermarking, Security, and Forensics 2014
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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