MFPD-LSTM: A steganalysis method based on multiple features of pitch delay using RNN-LSTM

Abstract

In recent years, adaptive multi-rate (AMR) steganalysis based on pitch delay has become a hot research area for researchers. The main reason is that the steganalysis algorithm based on AMR can sensitively detect the change in the statistical characteristics of the pitch delay before and after the secret information is embedded, and achieve high accuracy. At present, many excellent AMR steganalysis algorithms based on pitch delay have been proposed. However, when these algorithms are aimed at short-term, low-embedding-rate speech samples, there is room for improvement in accuracy and performance. In this paper, a Recurrent Neural Network-Long-Short Term Memory (RNN-LSTM) is used to design a steganalysis method through multi-feature fusion based on pitch delay (MF-PD). This method extracts the pitch delay sequence in the speech information stream, mines four features that characterize the statistical feature of the intra-frame and the inter-frame, and establishes the feature matrix. Then construct the RNN-LSTM model, the dropout layer is introduced to avoid over-fitting and the model is pruned to improve the efficiency. Experimental results show that when the speech sample length is 0.1s, this method can achieve a detection accuracy of more than 87%, which is significantly higher than other steganalysis algorithms. For samples with low embedding rate, the method proposed in this paper can also achieve better performance, which satisfies the need for short time and low embedding rate sample detection.