Audio steganalysis using deep belief networks

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 ve...

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Bibliographic Details
Published in:International journal of speech technology 2016-09, Vol.19 (3), p.585-591
Main Authors: Paulin, Catherine, Selouani, Sid-Ahmed, Hervet, Éric
Format: Article
Language:English
Subjects:
Artificial Intelligence
Audio data
Belief networks
Classification
Classifiers
Engineering
Robustness
Signal,Image and Speech Processing
Social Sciences
Speech
Steganography
Support vector machines
Tasks
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Summary: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.
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-016-9352-6