Audio steganalysis using deep belief networks.

Autor:	Paulin, Catherine, Selouani, Sid-Ahmed, Hervet, Éric
Předmět:	SOUND systems CRYPTOGRAPHY SUPPORT vector machines GAUSSIAN mixture models SPEECH processing systems SIGNALS & signaling
Zdroj:	International Journal of Speech Technology; Sep2016, Vol. 19 Issue 3, p585-591, 7p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink