Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward...Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.展开更多
基金Supported by the National Natural Science Foundation of China (No.60772032)
文摘Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.
