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.
Traditional linguistic stegosystems are based on modification of an existing cover text, e.g., using synonym substitution @cite_21 @cite_19 and or paraphrase substitution @cite_16 . The idea is to encode the secret information in the transformation of the cover text, ideally without affecting its meaning or grammatical correctness. Of these systems, the most closely related to ours is CoverTweet @cite_14 , a state-of-the-art cover modification stegosystem that uses Twitter as the medium of cover; we compare to it in our preliminary evaluation ( ).
@cite_19 @cite_14 @cite_21 @cite_16
2162174114 2074754392 2108498102 32140476
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. Information-hiding in natural language text has mainly consisted of carrying out approximately meaning-preserving modifications on the given cover text until it encodes the intended mark. A major technique for doing so has been synonym-substitution. In these previous schemes, synonym substitutions were done until the text "confessed", i.e., carried the intended mark message. We propose here a better way to use synonym substitution, one that is no longer entirely guided by the mark-insertion process: It is also guided by a resilience requirement, subject to a maximum allowed distortion constraint. Previous schemes for information hiding in natural language text did not use numeric quantification of the distortions introduced by transformations, they mainly used heuristic measures of quality based on conformity to a language model (and not in reference to the original cover text). When there are many alternatives to carry out a substitution on a word, we prioritize these alternatives according to a quantitative resilience criterion and use them in that order. In a nutshell, we favor the more ambiguous alternatives. In fact not only do we attempt to achieve the maximum ambiguity, but we want to simultaneously be as close as possible to the above-mentioned distortion limit, as that prevents the adversary from doing further transformations without exceeding the damage threshold; that is, we continue to modify the document even after the text has "confessed" to the mark, for the dual purpose of maximizing ambiguity while deliberately getting as close as possible to the distortion limit. The quantification we use makes possible an application of the existing information-theoretic framework, t This paper describes a method for checking the acceptability of paraphrases in context. We use the Google n-gram data and a CCG parser to certify the paraphrasing grammaticality and fluency. We collect a corpus of human judgements to evaluate our system. The ultimate goal of our work is to integrate text paraphrasing into a Linguistic Steganography system, by using paraphrases to hide information in a cover text. We propose automatically generated paraphrases as a new and useful source of transformations for Linguistic Steganography, and show that our method for checking paraphrases is effective at maintaining a high level of imperceptibility, which is crucial for effective steganography. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.