FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis of VoIP Stream via Multi-head Attention
This work addresses the need for efficient real-time steganalysis in VoIP communications, offering a domain-specific incremental improvement.
The paper tackles the problem of detecting steganography in VoIP streams by proposing FCEM, a lightweight neural network based on multi-head attention, which improves detection accuracy and doubles the speed for short samples compared to existing RNN and CNN models.
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.