Research on Uncertainty of audio and Video Information Hiding Based on Semantic and Statistical Moment

nowadays, audio and video media data is already facilitates generation, transmission, storage and circulation on the global scale. Audio and video data is geometrically fast as the rate of growth, the video data processing and  analysis have lagged behind the pace of development in the growth of data, resulting in large amounts of data is wasted. Therefore, it becomes an urgent need for efficient retrieval of video data content. Accordingly the SS hiding effectively, verify the presence of the secrete message in an important issue. In this paper we present two statistical analysis algorithms for SS hiding.  Both the two methods are based on machine learning theory and discrete wavelet transform (DWT), which adopts the classification technology.  In the algorithm I, we introduce Gaussian mixture model (GMM) and generalize Gaussian distribution (GGD) to character the probability distribution of wavelet sub-band. Then the absolute probability distribution function (PDF) moment is extracted as feature vectors. We use GMM to model the probability distribution of wavelet coefficient and calculate the absolute moment of statistical distribution as feature vector of each sub-band for statistical analysis. In the algorithm II, we propose distance metric between GMM and GGD of wavelet sub-band to distinguish cover. The experiment results of both two proposed classification algorithms may obtain better detecting performance. The probability distribution model takes GMM and GGD. We use de-noising method to get the estimation of cover audio, and then use four distances metric to measure the distortion.