Research On Audio Steganography And Steganalysis Techniques

Information Hiding (steganography), a new technology in information security fields, interests many researchers in the world. The goal of steganography is to hide the existence of information, steganalysis is a technology to attack the information hiding system and detect the hiding information. Audio steganography and steganalysis technology are studied in this paper. On the basis of the analysis of main theoretical problem and the research of some technology problems, some algorithms are proposed to improve the information hiding capacity, transparency, robustness and the accuracy of wavelet domain speech steganalysis.An echo multi-ary data hiding system is proposed to overcome the disadvantages of low hiding capacity of echo hiding system. The influence of key parameters of the system to the restoration rate of information is studied, such as delay time, segment length, and decay amplitude. By introducing forward-backward echo kernel with 32 time delays, a 32-ary information hiding system is realized and the hiding capacity is five times of single echo hiding system. When the sample rate of cover-audio is 8 kHz, restoration rate of information reaches to 99%. The robustness of the steganography system is simulated too, such as attacks of white noise, resampling (upsampling, down sampling), filtering (low pass filtering, high pass filtering), ADPCM compressing.An adaptive speech information hiding method, in which its embedding amplitude is controlled by audio quality assessments and psychology model, is proposed. This method can overcome the disadvantages of subjective listening in judging the transparency of stego-audio. The procedure of the algorithm from information embedding to information detecting is dressed in detail. At last, the embedding capacity, transparency, robustness performances are studied and compared with the constant amplitude embedding method. Simulation results show that this algorithm is effective.Audio steganalysis technologies for wavelet domain embedding methods are studied, the difference of characteristic features after information embedded is utilized to determine if information is embedded to the audio signal. Two effective classification features are proposed for additive noise embedding model in wavelet domain. They are speech wavelet subband coefficients histogram union features and wavelet domain amplitude co-occurrence matrix features. These two features are extracted and used to classify the audio signal by BP neural networks. Simulation results show the effectiveness of these two features. For multiplicative noise embedding model in wavelet domain, high order statistical moments features, wavelet subband coefficients histogram union features and wavelet domain amplitude co-occurrence matrix features have low accuracy in classification. Homomorphic processing is used to overcome the problem of low steganalysis accuracy. The test audio signal is firstly calculated its absolute value and logarithm. Multiplicative noise is changed to additive noise and the classification accuracy is improved.A wavelet domain audio steganalysis system based on feature selection technology is constructed. Classification features of audio signals to be analyzed are extracted. And then these features are processed by the principle component analysis or the factor analysis methods. The dimensions of these features are reduced evidently. Support vector machine is used as the classifier to classify the cover-audio and the stego-audio. Simulation results show that the detection rate reaches to 95% for three wavelet domain steganography methods.