RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model

Yang, Zhongliang; Zhang, Pengyu; Jiang, Minyu; Huang, Yudong; Zhang, Yujin

doi:10.1007/978-3-030-00012-7_24

Cited by 25 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, most of the steganographic text automatic generation models are under the following framework: using a welldesigned model to learn the statistical language model from a large number of normal sentences, and then implementing secret information hiding by encoding the conditional probability distribution of each word in the text generation process [9], [17]- [20], [24]. In this framework, the early works mainly use Markov model to approximate the language model and calculate the conditional probability distribution of each word [23], [24].…”

Section: Related Workmentioning

confidence: 99%

“…However, due to the limitations of Markov model itself [9], the quality of the text generated by Markov model is still not good enough, which makes it easy to be recognized. In recent years, with the development of natural language processing technology, more and more steganographic text generation models based on neural network models have emerged [9], [17]- [20]. T. Fang et al [18] first divide the dictionary and fixedly encode each word, and then use the recurent neural network (RNN) to learn the statistical language model of natural text.…”

Section: Related Workmentioning

confidence: 99%

“…These neural network based text automatic generation methods can better fit the statistical language model of normal sentences than Markov models, so that the quality of generated steganographic texts have been significantly improved [9], [17]- [20]. However, it still seems not enough.…”

Section: Related Workmentioning

confidence: 99%

“…In order to solve this challenge, previous researchers have done a lot of works [9], [16]- [20]. From the first generation of steganography based on specific syntax structure [13], to the later generation of steganography based on Markov model [14], [23], [24], and the recent emergence of a method of steganography based on neural network [9], [17]- [20], researchers have gradually been able to generate high-quality steganographic sentences. The current solution is mainly based on the following technical framework: Alice first uses a well-designed model (such as recurrent neural network [9]) to learn a statistical language model of a large number of natural texts.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Yang,

Gong,

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Most of the existing text generative steganographic methods are based on coding the conditional probability distribution of each word during the generation process, and then selecting specific words according to the secret information, so as to achieve information hiding. Such methods have their limitations which may bring potential security risks. Firstly, with the increase of embedding rate, these models will choose words with lower conditional probability, which will reduce the quality of the generated steganographic texts; secondly, they can not control the semantic expression of the final generated steganographic text. This paper proposes a new text generative steganography method which is quietly different from the existing models. We use a Knowledge Graph (KG) to guide the generation of steganographic sentences. On the one hand, we hide the secret information by coding the path in the knowledge graph, but not the conditional probability of each generated word; on the other hand, we can control the semantic expression of the generated steganographic text to a certain extent. The experimental results show that the proposed model can guarantee both the quality of the generated text and its semantic expression, which is a supplement and improvement to the current text generation steganography.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Yang,

Gong,

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…While for Eve, her task is to accurately determine whether the carrier contains hidden information, so she needs to find the difference as much as possible in the statistical distribution of the carrier before and after steganography. There are various media forms of carrier that can be used for information hiding, including image [178], audio [53], [169], text [13], [197] and so on [179]. In recent years, with the popularity and development of the Internet, communication based on streaming media has been greatly developed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows

Yang¹,

Yang²,

Hu³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

With the development and popularization of network technology, communication using Voice over IP (VoIP) has become an increasingly common means of information transmission in people's daily lives. With these emerging communication channels, more and more VoIP-based steganography methods have appeared in recent years, which pose a great threat to the security of cyberspace. Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolutional Neural Network (CNN), we propose a real-time VoIP steganalysis method which based on multi-channel convolutional sliding windows (CSW). In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multichannel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolutional layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not. We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.

show abstract

Recent Trends in Text Steganography with Experimental Study

Kumar

Singh

2020

Handbook of Computer Networks and Cyber Security

View full text Add to dashboard Cite

RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model

Cited by 25 publications

References 14 publications

Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows

Recent Trends in Text Steganography with Experimental Study

Contact Info

Product

Resources

About