Efficient Parallel Feature Selection for Steganography Problems
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Inteligencia artificialArtificial intelligence
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]
SponsorshipTIN2007-60587, P07-TIC-02768 and P07-TIC-02906,TIC-3928; Nokia Foundation, Finland
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.