3 research outputs found
FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis of VoIP Stream via Multi-head Attention
Extracting correlation features between codes-words with high computational
efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this
paper, we utilized attention mechanisms, which have recently attracted enormous
interests due to their highly parallelizable computation and flexibility in
modeling correlation in sequence, to tackle steganalysis problem of
Quantization Index Modulation (QIM) based steganography in compressed VoIP
stream. We design a light-weight neural network named Fast Correlation Extract
Model (FCEM) only based on a variant of attention called multi-head attention
to extract correlation features from VoIP frames. Despite its simple form, FCEM
outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional
Neural Networks (CNNs) models on both prediction accuracy and time efficiency.
It significantly improves the best result in detecting both low embedded rates
and short samples recently. Besides, the proposed model accelerates the
detection speed as twice as before when the sample length is as short as 0.1s,
making it a excellent method for online services.Comment: 5 pages, 2 figures. accepted by ICASSP'202
Text Steganalysis with Attentional LSTM-CNN
With the rapid development of Natural Language Processing (NLP) technologies,
text steganography methods have been significantly innovated recently, which
poses a great threat to cybersecurity. In this paper, we propose a novel
attentional LSTM-CNN model to tackle the text steganalysis problem. The
proposed method firstly maps words into semantic space for better exploitation
of the semantic feature in texts and then utilizes a combination of
Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)
recurrent neural networks to capture both local and long-distance contextual
information in steganography texts. In addition, we apply attention mechanism
to recognize and attend to important clues within suspicious sentences. After
merge feature clues from Convolutional Neural Networks (CNNs) and Recurrent
Neural Networks (RNNs), we use a softmax layer to categorize the input text as
cover or stego. Experiments showed that our model can achieve the state-of-art
result in the text steganalysis task.Comment: 5 pages, 1 figures, accepted by ICCCS'201
Fast Steganalysis Method for VoIP Streams
In this letter, we present a novel and extremely fast steganalysis method of
Voice over IP (VoIP) streams, driven by the need for a quick and accurate
detection of possible steganography in VoIP streams. We firstly analyzed the
correlations in carriers. To better exploit the correlation in code-words, we
mapped vector quantization code-words into a semantic space. In order to
achieve high detection efficiency, only one hidden layer is utilized to extract
the correlations between these code-words. Finally, based on the extracted
correlation features, we used the softmax classifier to categorize the input
stream carriers. To boost the performance of this proposed model, we
incorporate a simple knowledge distillation framework into the training
process. Experimental results show that the proposed method achieves
state-of-the-art performance both in detection accuracy and efficiency. In
particular, the processing time of this method on average is only about 0.05\%
when sample length is as short as 0.1s, attaching strong practical value to
online serving of steganography monitor.Comment: 5 pages, 2 figure