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.

本研究利用关注机制处理定量化指数调制压缩 VoIP 流中基于 QIM 的隐写分析问题。研发了一个名为快速相关提取模型（FCEM）的轻量级神经网络来提取 VoIP 帧中的相关特征，具有比复杂的循环神经网络和卷积神经网络更高的准确性和时间效率。此外，提出的模型在样品长度为 0.1s 时可将检测速度提高一倍，适用于在线服务。