Speech steganalysis using evolutionary restricted Boltzmann machines

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jul 2016Publisher:IEEEJournal:2016 IEEE Congress on Evolutionary Computation (CEC)

Authors: Catherine Paulin; Sid-Ahmed Selouani; Eric Hervet;

doi: 10.1109/cec.2016.7744409

Speech steganalysis using evolutionary restricted Boltzmann machines

- Summary
- Metrics

Abstract

This paper presents a new method to train Restricted Boltzmann Machines (RBMs) using Evolutionary Algorithms (EAs), where RBMs are used in the first step of a steganalysis tool for speech/audio files. The following EAs have been tested: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bees Colony (ABC) and Cat Swarm Optimization (CSO). Our method has been tested with three steganographic techniques: StegHide, Hide4PGP, and FreqSteg. A fourth technique combining the three steganographic methods has also been tested. The results are compared to the conventional contrastive divergence learning algorithm. All EAs outperform the contrastive divergence algorithm.

Related Organizations

Université de Moncton
Canada

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	10
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average