  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.
