Efficient Parallel Feature Selection for Steganography Problems

Authors:
Alberto Guillén;Antti Sorjamaa;Yoan Miche;Amaury Lendasse;Ignacio Rojas
Affiliations:
Department of Informatics, University of Jaen, Spain;Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Computer Architecture and Technology, University of Granada, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

Citing 4
Cited 2

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Defending against statistical steganalysis

SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Methodology for long-term prediction of time series

Neurocomputing
Parallel multi-objective memetic RBFNNs design and feature selection for function approximation problems

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks

New method for instance or prototype selection using mutual information in time series prediction

Neurocomputing
Massively parallel feature selection: an approach based on variance preservation

ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The steganography problem consists of the identification of images hiding a secret message, which cannot be seen by visual inspection. This problem is nowadays becoming more and more important since the World Wide Web contains a large amount of images, which may be carrying a secret message. Therefore, the task is to design a classifier, which is able to separate the genuine images from the non-genuine ones. However, the main obstacle is that there is a large number of variables extracted from each image and the high dimensionality makes the feature selection mandatory in order to design an accurate classifier. This paper presents a new efficient parallel feature selection algorithm based on the Forward-Backward Selection algorithm. The results will show how the parallel implementation allows to obtain better subsets of features that allow the classifiers to be more accurate.