Efficient Parallel Feature Selection for Steganography Problems
Identificadores
URI: https://hdl.handle.net/10481/77911Metadatos
Mostrar el registro completo del ítemEditorial
Springer
Materia
Inteligencia artificial Artificial intelligence
Fecha
2009Referencia bibliográfica
Published version: Guillén, A... [et al.] (2009). Efficient Parallel Feature Selection for Steganography Problems. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-02478-8_153]
Patrocinador
TIN2007-60587, P07-TIC-02768 and P07-TIC-02906,TIC-3928; Nokia Foundation, FinlandResumen
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.