An autoregressive time delay neural network for speech steganalysis

dc.contributor.author Rekik, Siwar
dc.contributor.author Selouani, Sid-Ahmed
dc.contributor.author Guerchi, Driss
dc.contributor.author Hamam, Habib
dc.date.accessioned 2020-01-30T05:56:40Z
dc.date.available 2020-01-30T05:56:40Z
dc.date.copyright 2012 en_US
dc.date.issued 2012
dc.description This conference paper is not available at CUD collection. The version of scholarly record of this Conference Paper is published in 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) (2012), available online at: https://ieeexplore.ieee.org/document/6310612. en_US
dc.description.abstract Hiding a secret message in speech signal, called steganography, is used to provide secure communication. The detection of hidden information in the transmitted message called steganalysis. The purpose of steganalysis is to identify the presence of embedded information, and does not actually attempt to extract or decode the hidden data. An automated method is required for detecting the existence of hidden message, since the huge amount of channeled information. However, the development and evaluation of steganalysis algorithms is a challenging task. In this paper we advocate a new steganalysis technique to classify a speech as having hidden information or not, using a powerful and sophisticated classifier called Autoregressive Time Delay Neural Network (AR-TDNN). The originality of this AR-TDNN is its quite ability to detect secret messages hidden with different steganographic algorithms, although the variation of detection rate depends on the particular hiding techniques and amount of hidden information. © 2012 IEEE. en_US
dc.identifier.citation Rekik, S., Selouani, S.-A., Guerchi, D., & Hamam, H. (2012). An autoregressive time delay neural network for speech steganalysis. In 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 (pp. 54–58). https://doi.org/10.1109/ISSPA.2012.6310612 en_US
dc.identifier.isbn 9781467303828
dc.identifier.uri http://dx.doi.org/10.1109/ISSPA.2012.6310612
dc.identifier.uri https://hdl.handle.net/20.500.12519/81
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Authors Affiliations: Rekik, S., Université de Bretagne, Occidentale, Brest, 29200, France; Selouani, S.-A., University of Moncton, 218 Bvd. J-D. Gauthier, E8S 1P6 Shippagan, Canada; Guerchi, D., Canadian University of Dubai, Sheikh Zayed Road, Dubai, United Arab Emirates; Hamam, H., University of Moncton, Campus d'Edmundston, E3V 2S8, Canada
dc.relation.ispartofseries 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012;
dc.rights Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holder Copyright : 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subject Auto-regressive en_US
dc.subject Automated methods en_US
dc.subject Data hiding en_US
dc.subject Detection rates en_US
dc.subject Embedded information en_US
dc.subject Hidden information en_US
dc.subject Hidden messages en_US
dc.subject Secret messages en_US
dc.subject Secure communications en_US
dc.subject Speech signals en_US
dc.subject Steganalysis en_US
dc.subject Steganographic algorithms en_US
dc.subject Time delay neural networks en_US
dc.subject Algorithms en_US
dc.subject Computer simulation en_US
dc.subject Information science en_US
dc.subject Neural networks en_US
dc.subject Time delay en_US
dc.subject Steganography en_US
dc.title An autoregressive time delay neural network for speech steganalysis en_US
dc.type Conference Paper en_US
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