Steganalysis of VoIP Streams with CNN-LSTM Network

Abstract

Steganalysis of the Quantization Index Modulation (QIM) steganography in VoIP (Voice-over IP) stream is conducted in this research. VoIP is a popular media streaming and communication service on the Internet. QIM steganography makes it possible to hide secret information in VoIP streams. Detecting short and low embedding rates of QIM steganography samples remains an unsolved challenge. Recently, neural network models have been demonstrated to be capable of achieving remarkable performances and be successfully applied to many different tasks. The mainstream architectures of neural network include Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which adopt totally different ways to understand various signals. In this paper, we first indicate a proper way to combine the strengths of these two architectures and then construct a novel and unified model called CNN-LSTM network to detect QIM-based steganography. In our model, Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is utilized to capture long time contextual information in carriers and CNN was used subsequently to capture both local features and global ones as well as temporal carrier features. Experiments showed that our model can achieve the state-of-art result in detecting QIM-based steganography in VoIP streams.