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Extracting correlation features between codes-words with high computationalefficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In thispaper, we utilized attention mechanisms, which have recently attracted enormousinterests due to their highly parallelizable computation and flexibility inmodeling correlation in sequence, to tackle steganalysis problem ofQuantization Index Modulation (QIM) based steganography in compressed VoIPstream. We design a light-weight neural network named Fast Correlation ExtractModel (FCEM) only based on a variant of attention called multi-head attentionto extract correlation features from VoIP frames. Despite its simple form, FCEMoutperforms complicated Recurrent Neural Networks (RNNs) and ConvolutionalNeural Networks (CNNs) models on both prediction accuracy and time efficiency.It significantly improves the best result in detecting both low embedded ratesand short samples recently. Besides, the proposed model accelerates thedetection speed as twice as before when the sample length is as short as 0.1s,making it a excellent method for online services.

FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis of VoIP Stream via Multi-head Attention