  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.
