Research On Audio Steganalysis

Audio steganalysis is a countermeasure technology of audio steganography,which plays an important role in the field of combating steganalysis in network attacks and preventing the spread of harmful information by audio steganalysis.At present,the research on audio steganalysis has made some achievements,but the further development of audio steganalysis technology also puts forward higher requirements for audio steganalysis technology,which promotes the development of audio steganalysis technology.By focusing on the audio steganalysis technology in non-compressed domain and MP3 compressed domain and analyzing the influence of audio steganalysis methods on carrier audio,combined with ensemble learning and convolutional neural network,some improved audio steganalysis algorithms for non-compressed domain and MP3 compressed domain are proposed,which further improves the detection accuracy of audio steganalysis.The main work and innovation are as follows:1)A general audio steganalysis method based on piecewise entropy is proposed.At present,the universal steganalysis of uncompressed domain audio is to calculate its inherent characteristics(such as MFCC,LPCC,etc.)on the full band audio signal,and this process will reduce the impact of the embedded hidden information on the carrier signal.In addition,support vector machine is used as the classification algorithm,so the training time is too long.Firstly,the influence of steganography on the entropy of audio signal is analyzed,the steganalysis feature based on entropy is proposed,and then the FLD ensemble learning method is applied for steganalysis as a classifier.The detection accuracy of the four steganography methods is more than 87%,and in the case of blind detection,the detection accuracy is more than 86%.Compared with the SVM comparison method,the training time is shortened by 75%,which greatly speeds up the training and detection speed.2)A special echo steganalysis method based on improved statistical features and FLD ensemble learning method is proposed.The features selected by current general steganalysis methods have low discrimination for echo steganography,which can not well reflect the impact of echo steganalysis information on carrier audio impression,and the detection accuracy for echo steganalysis is low.Firstly,echo steganography is analyzed and the improved statistical feature with discrimination is proposed,and then apply the feature to FLD based ensemble learning method.The experimental results show that the accuracy of this method is more than90.69%.At the same time,the detection accuracy of bipolar and bidirectional echo steganography algorithm in all echo amplitudes is more than 86%.3)A steganalysis method in MP3 compressed domain based on improved convolutional neural network is proposed.In view of the poor performance of the traditional manual feature extraction method in the analysis of MP3 steganographic audio.An improved convolutional neural network structure based on the basic convolutional neural network is proposed.In this network structure,a high pass filter layer is introduced to suppress the influence of audio carrier signal itself on steganalysis.At the same time,a 1 * 1 convolution core is introduced for cross channel information integration,and a BN layer is introduced to prevent over fitting.The detection accuracy of this method is still more than 70% in the lower embedding rate,and when the embedding rate is slightly higher,the accuracy is greatly improved to more than 80%.Compared with the manual feature extraction method and the residual network-based method,the accuracy of this method is improved by more than 5%.