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LZW-CIE: a high-capacity linguistic steganography based on LZW char index encoding

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Abstract

With the effect of digitalization, the transfer of all text documents over the Internet rather than human transmission has increased, and this situation has revealed the idea that text documents can be used as a carrier that can safely store information. Realizing that methods such as word-line shifting, usage of spaces, replacement of the word with its synonym are fragile against steganalysis, led to new searches and it was determined that deep learning models were more resistant to detecting the presence of hidden words. In this study, the text generation based on the information that is wanted to be hidden without a carrier text, both at word and character level, was performed. Arithmetic coding, perfect tree and Huffman coding methods were used as secret information embedding methods in text generation based on word level. In this part of the study, bidirectional LSTM architecture with attention mechanism was created as language model. In text generation based on character level, a new secret information embedding algorithm is created by combining the LZW compression algorithm with the Char Index (LZW-Char Index Encoding) method. The character-level model is created as a result of using the encoder–decoder architecture together with bidirectional LSTM and Bahdanau attention. The proposed method was evaluated from the perspectives of information embedding efficiency, information imperceptibility and hidden information capacity. As a result of the experiments, it was determined that the method exceeded the state-of-the-art performance and was more resistant to steganalysis.

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  1. Faculty of Technology Information Systems Engineering, Burdur Mehmet Akif Ersoy Üniversitesi, 15300, Burdur, Turkey

    Merve Varol Arısoy

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Author Merve Varol Arısoy has received assistance from Professor Dr. Ecir Uğur Küçüksille only in terms of sharing information about solving the problems encountered in the project and providing the necessary guidance during the realization of the study. Except this, the author has no competing interests to declare that are relevant to the content of this article.

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Varol Arısoy, M. LZW-CIE: a high-capacity linguistic steganography based on LZW char index encoding. Neural Comput & Applic 34, 19117–19145 (2022). https://doi.org/10.1007/s00521-022-07499-5

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