An autoregressive time delay neural network for speech steganalysis
Date
2012
Journal Title
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Publisher
Institute of Electrical and Electronics Engineers Inc.
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.
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.
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Conference Paper
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Keywords
Auto-regressive, Automated methods, Data hiding, Detection rates, Embedded information, Hidden information, Hidden messages, Secret messages, Secure communications, Speech signals, Steganalysis, Steganographic algorithms, Time delay neural networks, Algorithms, Computer simulation, Information science, Neural networks, Time delay, Steganography
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