A BLIND AUDIO STEGANALYSIS BASED ON FEATURE FUSION  

作　　者：Wei Yifang Guo Li Wang Yujie Wang Cuiping 

机构地区：[1]Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China

出　　处：《Journal of Electronics(China)》2011年第3期265-276,共12页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61071173)

摘　　要：In this paper, we present a blind steganalysis based on feature fusion. Features based on Short Time Fourier Transform (STFT), which consists of second-order derivative spectrum features of audio and Mel-frequency cepstrum coefficients, audio quality metrics and features on linear prediction residue are extracted separately. Then feature fusion is conducted. The performance of the proposed steganalysis is evaluated against 4 steganographic schemes: Direct Sequence Spread Spectrum (DSSS), Quantization Index Modulation (QIM), ECHO embedding (ECHO), and Least Significant Bit em-bedding (LSB). Experiment results show that the classifying performance of the proposed detector is much superior to the previous work. Even more exciting is that the proposed methodology could detect the four steganography, with 85%+ classification accuracy achieved in all the detections, which makes the proposed steganalysis methodology capable of being regarded as a blind steganalysis, and especially useful when the steganalyzer are without the knowledge of the steganographic scheme employed in data embedding.In this paper, we present a blind steganalysis based on feature fusion. Features based on Short Time Fourier Transform （STFT）, which consists of second-order derivative spectrum features of audio and Mel-frequency cepstrum coefficients, audio quality metrics and features on linear prediction residue are extracted separately. Then feature fusion is conducted. The performance of the proposed steganalysis is evaluated against 4 steganographic schemes： Direct Sequence Spread Spectrum （DSSS）, Quantization Index Modulation （QIM）, ECHO embedding （ECHO）, and Least Significant Bit em-bedding （LSB）. Experiment results show that the classifying performance of the proposed detector is much superior to the previous work. Even more exciting is that the proposed methodology could detect the four steganography, with 85%＋ classification accuracy achieved in all the detections, which makes the proposed steganalysis methodology capable of being regarded as a blind steganalysis, and especially useful when the steganalyzer are without the knowledge of the steganographic scheme employed in data embedding.

关 键 词：Feature fusion STEGANALYSIS Mel-cepstrum Second-order derivative Audio quality metrics Linear prediction 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]