SSLSS: Semi-Supervised Learning-Based Steganalysis Scheme for Instant Voice Communication Network

In the field of instant voice communication (IVC) steganalysis, the traditional detecting methods are mainly based on supervised learning scheme that results in a large amount of complex manual pre-processing training data set. The accuracy of supervised learning scheme can be easily destroyed by the difference between the distribution of training and testing data set in the actual voice application. The disadvantages of this method are obvious in the big data environment. In this regard, this paper initially introduced a novel semi-supervised hybrid learning detection model for the IVC network. This provides the progress of manually annotating training data set that has been removed to solve the problem of complex operations and poor applicability in classifier. Therefore, this model has a simpler structure and more extensive detection scopes with the huge amount of data. Then, we designed a multi-criteria fusion module that can automatically generate the pseudo-label set from testing data set to train the classifier model. Thus, our scheme will not be affected by the distribution shift. In this module, we defined the confidence level and representative level to judge the feature vector for pseudo-labeled. Through the experimental analysis, the low bit-rate speech coding steganalysis (G.723.1/G.729/iLBC speech codecs) is analyzed on quantization index modulation that are common codecs in the IVC network. The results show that our method has higher accuracy than un-supervised method. The proposed approach is less affected and more accurate than the previous supervised methods through the distribution of different training and testing data sets. The experiments also proved that our method can be deployed in the different kinds of the IVC codec by considering huge amount of data set.