Abstract
Today, modern steganalysis needs to start using high-dimensional feature spaces, which makes the complexity of traditional classifiers such as support vector machine (SVM) increase rapidly. This paper proposes a frame of selective ensemble classifiers as an alternative to SVM for steganalysis by applying the selective theory that ensemble some instead of all the available base learners. A family of weak classifiers is built on random subspaces of the high-dimensional feature spaces. Then, assign a random weight to each classifier and employ genetic algorithm to evolve those weights based on a validation set. The Final classifier is constructed by fusing the decisions of individual classifiers whose weight is bigger than a pre-set threshold λ. Experiments with the steganographic algorithms nsF5 and MBS demonstrate the usefulness of the approach over current popular methods.
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Chen, B., Feng, G., Li, F. (2012). Steganalysis in High-Dimensional Feature Space Using Selective Ensemble Classifiers. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_2
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DOI: https://doi.org/10.1007/978-3-642-34595-1_2
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