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A blind audio steganalysis based on feature fusion

  • Published:
Journal of Electronics (China)

Abstract

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 embedding (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.

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Authors and Affiliations

  1. Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Yifang Wei, Li Guo, Yujie Wang & Cuiping Wang

  2. University of Science and Technology of China, Room 210, Diansi Building, West Campus, Hefei, 230027, China

    Li Guo

Authors
  1. Yifang Wei
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  2. Li Guo
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  3. Yujie Wang
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  4. Cuiping Wang
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Corresponding author

Correspondence to Li Guo.

Additional information

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

Communication author: Guo Li, born in 1946, male, Professor.

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Wei, Y., Guo, L., Wang, Y. et al. A blind audio steganalysis based on feature fusion. J. Electron.(China) 28, 265–276 (2011). https://doi.org/10.1007/s11767-011-0567-z

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  • DOI: https://doi.org/10.1007/s11767-011-0567-z

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