Speech steganalysis using evolutionary restricted Boltzmann machines
Catherine Paulin, Sid‐Ahmed Selouani, Éric Hervet · 2016
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.