A real time text steganalysis by using statistical method

Samanta, Sabyasachi; Dutta, Saurabh; Sanyal, Goutam

doi:10.1109/icetech.2016.7569256

Cited by 22 publications

(7 citation statements)

References 5 publications

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“…1) Steganalysis Accuracy: In this section, we compaired with three representative text steganalysis algorithms, which are proposed in [26], [27], [28], respectively. We use several evaluation indicators commonly used in classification tasks to evaluate the performance of our model, which are precision, recall, F1-score and accuracy.…”

Section: A Evaluation Results and Discussionmentioning

confidence: 99%

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

Yang

Wang

et al. 2019

IEEE Signal Process. Lett.

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With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful selflearning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this paper, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use Recurrent Neural Networks (RNNs) to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text.

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Section: A Evaluation Results and Discussionmentioning

confidence: 99%

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

Yang

Wang

et al. 2019

IEEE Signal Process. Lett.

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“…Several different representative steganalysis algorithms were chosen as our baseline models [18][19][20][21] to validate the performance of proposed model. e results of the comparison is shown in Table 1.…”

Section: Performance Evaluationmentioning

confidence: 99%

Neural Linguistic Steganalysis via Multi-Head Self-Attention

Jiao

Wang

Zhang

et al. 2021

Journal of Electrical and Computer Engineering

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Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.

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“…In addition, we used the techniques from Meng et al 19 and Samanta et al 20 to test the steganalysis resistibility of each model, with measurements of accuracy being the result of these tests. The stronger the resistibility of the model, the closer its resulting value is to 0.5.…”

Section: Inputmentioning

confidence: 99%

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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A real time text steganalysis by using statistical method

Cited by 22 publications

References 5 publications

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

Neural Linguistic Steganalysis via Multi-Head Self-Attention

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

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