基于多通道卷积滑动窗口的流媒体实时的隐写分析
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
摘要：
以往的VoIP隐写分析方法在低嵌入率下检测语音信号面临很大挑战，而且一般难以进行实时的【形】检测，难以真正维护网络空间的安全。针对这两个难题，本文结合滑动窗口检测算法和卷积神经网络，提出了一种基于多通道卷积滑动窗口的实时的【形】VoIP隐写分析方法。为了分析VoIP信号中帧与相邻帧之间的相关性，我们定义了多通道滑动检测窗。在每个滑动窗口内，我们设计了两个特征提取通道，其中包含多个卷积层，每层具有多个卷积核，以提取输入信号的相关特征。然后基于这些提取的特征，我们使用一个前向全连接网络进行特征融合。最后，通过分析这些特征的统计分布，判断输入语音信号中是否含有隐藏信息。我们设计了几个实验来测试所提出的模型在不同嵌入率、不同语音长度等条件下的检测能力。实验结果表明，所提出的模型优于以往的所有方法，特别是在低嵌入率的情况下，表现出最先进的性能。此外，我们还对该模型的检测效率进行了测试，结果表明该模型能够实现VoIP语音信号的几乎实时的【形】检测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
