Detection of Multiple Steganography Methods in Compressed Speech Based on Code Element Embedding, Bi-LSTM and CNN With Attention Mechanisms

Steganographic algorithms in low-bit-rate compressed speech bring convenience to realize covert communication, meanwhile result in safety issues. The existing steganalysis methods are normally designed for one specific category of steganographic methods, thus lacking generalization capability. In this paper, we propose a general steganalysis method based on code element (CE) embedding, Bi-LSTM and CNN with attention mechanisms. Firstly, CEs in each frame are converted to a multi-hot vector. And each multi-hot vector will be mapped into a fixed-length embedding vector to get a more compact representation by utilizing dictionaries. Then, Bi-LSTM and CNN are applied to extract the contextual information and the local characteristics respectively of these embedding vectors. In addition, the attention mechanisms are introduced in different layers of the network to assign different weights to the output feature within each layer. Finally, the prediction results can be generated by the fully connected layer. Experimental results show that our method performs better than the existing steganalysis methods for detecting multiple steganography methods in the low-bit-rate compressed speech streams.