3 research outputs found
Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding
Steganography, as one of the three basic information security systems, has
long played an important role in safeguarding the privacy and confidentiality
of data in cyberspace. The text is the most widely used information carrier in
people's daily life, using text as a carrier for information hiding has broad
research prospects. However, due to the high coding degree and less information
redundancy in the text, it has been an extremely challenging problem to hide
information in it for a long time. In this paper, we propose a steganography
method which can automatically generate steganographic text based on the Markov
chain model and Huffman coding. It can automatically generate fluent text
carrier in terms of secret information which need to be embedded. The proposed
model can learn from a large number of samples written by people and obtain a
good estimate of the statistical language model. We evaluated the proposed
model from several perspectives. Experimental results show that the performance
of the proposed model is superior to all the previous related methods in terms
of information imperceptibility and information hidden capacity.Comment: Submitted to IETE Technical Revie
Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph
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
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
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 Convolution Neural Network we propose a
real-time VoIP steganalysis method which based on multi-channel convolution
sliding windows. In order to analyze the correlations between frames and
different neighborhood frames in a VoIP signal, we define multi channel sliding
detection windows. Within each sliding window, we design two feature extraction
channels which contain multiple convolution 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.Comment: 13 pages, summit to ieee transactions on information forensics and
security (tifs