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Audio steganalysis using deep belief networks

Abstract

This paper presents a new steganalysis method that uses a deep belief network (DBN) as a classifier for audio files. It has been tested on three steganographic techniques: StegHide, Hide4PGP and FreqSteg. The results were compared to two other existing robust steganalysis methods based on support vector machines (SVMs) and Gaussian mixture models (GMMs). Afterwards, another classification task aiming at identifying the type of steganographic applied or not to the speech signal was carried out. The results of this four-way classification show that in most cases, the proposed DBN-based steganalysis method gives higher classification rates than the two other steganalysis methods based on SVMs and GMMs.

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Correspondence to Catherine Paulin.

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Paulin, C., Selouani, SA. & Hervet, É. Audio steganalysis using deep belief networks. Int J Speech Technol 19, 585–591 (2016). https://doi.org/10.1007/s10772-016-9352-6

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Keywords

  • Audio steganography
  • Audio steganalysis
  • DBN
  • MFCCs
  • SVMs
  • GMMs