Text Steganography System Using Markov Chain Source Model and DES Algorithm

Dai, Weihui; Yu, Yue; Dai, Yonghui; Deng, Bin

doi:10.4304/jsw.5.7.785-792

Cited by 34 publications

(15 citation statements)

References 3 publications

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“…In order to test the performance of the proposed method, we selected three coverless text steganography methods to train the model on three datasets and compared them. The two steganography models in Dai et al 12 and Hernan Moraldo 14 are similar to the model proposed in this article, both of which are based on the Markov chain. The model in Yang et al 6 is based on the neural network.…”

Section: Inputmentioning

confidence: 53%

STBS-Stega: Coverless text steganography based on state transition-binary sequence

Yang

et al. 2020

International Journal of Distributed Sensor Networks

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Information-hiding technology has recently developed into an area of significant interest in the field of information security. As one of the primary carriers in steganography, it is difficult to hide information in texts because there is insufficient information redundancy. Traditional text steganography methods are generally not robust or secure. Based on the Markov chain model, a new text steganography approach is proposed that focuses on transition probability, one of the most important concepts of the Markov chain model. We created a state transition-binary sequence diagrams based on the aforementioned concepts and used them to guide the generation of new texts with embedded secret information. Compared to other related works, the proposed method exploits the use of the transition probability in the process of steganographic text generation. The associated developed algorithm also encrypts the serial number of the state transition-binary sequence diagram needed by the receiver to extract the information, which further enhances the security of the steganography information. Experiments were designed to evaluate the proposed model. The results revealed that the model had higher concealment and hidden capacity compared to previous methods.

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Section: Inputmentioning

confidence: 53%

STBS-Stega: Coverless text steganography based on state transition-binary sequence

Yang

et al. 2020

International Journal of Distributed Sensor Networks

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“…Namely, given a string of past words, the language model provides an estimate of the probability that any given word from an pre-defined vocabulary will be the next word. The popular language model employed in text generation-based linguistic steganography is the Markov model [5,12,32,33,42,43].…”

Section: Text Generation-based Linguistic Steganographymentioning

confidence: 99%

“…It is assumed that all transition probabilities from a given state to other state are equal. Reference [43] cooperated the Markov chain model with the DESalgorithm to enhance the security of the secret information and presented a fixed-length coding method to encode each candidate word. However, in the process of stego text generation, they ignored the transition probability of each word, leading to the generated stego text having poor quality.…”

Section: Text Generation-based Linguistic Steganographymentioning

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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“…It can hide 10 bits in each end tag of page line. In Reference [16], a text steganography system for spelling languages is proposed based on Markov Chain source model and DES algorithm. It can work reliably with the capability of immunity from regular operations, such as formatting, compressing and sometimes manual altering operation in text size, front, color and the space between words.…”

Section: Introductionmentioning

confidence: 99%

A Text Information Hiding Algorithm Based on Alternatives

Liu¹,

Ding²,

Su³

et al. 2013

JSW

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In this paper, we analyze existing text-based steganography techniques, and propose an efficient information hiding algorithm for text based on substituted conception. In this algorithm, candidate units are replaced by similar conception, including synonym, homonym or special character. An improved encoding algorithm is also proposed which is not only enhances the robustness, but also enlarges the capacity of secret message. It is proved by the experiments that the proposed algorithm has good performances both in security and capacity.

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Text Steganography System Using Markov Chain Source Model and DES Algorithm

Abstract:
High transmission efficiency, low resource occupancy Show more

Cited by 34 publications

References 3 publications

STBS-Stega: Coverless text steganography based on state transition-binary sequence

STBS-Stega: Coverless text steganography based on state transition-binary sequence

Novel Linguistic Steganography Based on Character-Level Text Generation

A Text Information Hiding Algorithm Based on Alternatives

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Text Steganography System Using Markov Chain Source Model and DES Algorithm

Abstract: High transmission efficiency, low resource occupancy Show more

Cited by 34 publications

References 3 publications

STBS-Stega: Coverless text steganography based on state transition-binary sequence

STBS-Stega: Coverless text steganography based on state transition-binary sequence

Novel Linguistic Steganography Based on Character-Level Text Generation

A Text Information Hiding Algorithm Based on Alternatives

Contact Info

Product

Resources

About

Abstract:
High transmission efficiency, low resource occupancy Show more