Generating steganographic image description by dynamic synonym substitution

Li, Mengdi; Kai, Mu; Zhong, Ping; Wen, Juan; Xue, Yiming

doi:10.1016/j.sigpro.2019.06.014

Cited by 18 publications

(2 citation statements)

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“…Linguistic steganography Linguistic steganography has been an active research area for decades, which is typically grouped into modification-based methods and generation-based methods. The modification-based methods embed the secret information mostly by substituting synonyms [22]- [24] or rewriting phrases [25]. They are not secure enough since the statistical distribution of generated stego texts is changed seriously.…”

Section: Related Workmentioning

confidence: 99%

Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

et al. 2023

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Deep learning based language models have improved generation-based linguistic steganography, posing a huge challenge for linguistic steganalysis. The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features. To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-shortterm-memory (BiLSTM) and short-cut dense convolutional neural network (CNN). The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence. The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix. We connect two structures in parallel, concatenate the long dependency representations and the local semantic features, and classify the stego and cover texts. The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.

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Section: Related Workmentioning

confidence: 99%

Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

et al. 2023

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“…Linguistic steganography based on text modification takes advantage of equivalent linguistic transformations to slightly modify the text content to hide the secret message while preserving the meaning of the original text. The linguistic transformations include syntactic transformations [10,13], synonym substitutions [14][15][16][17], misspelled word substitutions [18] and so on. This type of linguistic steganography has high imperceptibility, but limited embedding capacity, as the alternative transformations in a text are always very rare.…”

Section: Introductionmentioning

confidence: 99%

Novel Linguistic Steganography Based on Character-Level Text Generation

et al. 2020

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With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down.

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

Arısoy

2022

Neural Comput & Applic

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Generating steganographic image description by dynamic synonym substitution

Cited by 18 publications

References 10 publications

Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

Novel Linguistic Steganography Based on Character-Level Text Generation

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

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