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Bazi, Y., Alajlan, N., Melgani, F., AlHichri, H., Malek, S. and Yager, R.R. (2014) Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters, 11, 1066-1070.
https://doi.org/10.1109/LGRS.2013.2286078

has been cited by the following article:

• TITLE: A Survey on Different Feature Extraction and Classification Techniques Used in Image Steganalysis

  AUTHORS: John Babu, Sridevi Rangu, Pradyusha Manogna

  KEYWORDS: Steganalysis, Steganography, Feature Extraction, Classification

  JOURNAL NAME: Journal of Information Security, Vol.8 No.3, July 14, 2017

  ABSTRACT: Steganography is the process of hiding data into public digital medium for secret communication. The image in which the secret data is hidden is termed as stego image. The detection of hidden embedded data in the image is the foundation for blind image steganalysis. The appropriate selection of cover file type and composition contribute to the successful embedding. A large number of steganalysis techniques are available for the detection of steganography in the image. The performance of the steganalysis technique depends on the ability to extract the discriminative features for the identification of statistical changes in the image due to the embedded data. The issue encountered in the blind image steganography is the non-availability of knowledge about the applied steganography techniques in the images. This paper surveys various steganalysis methods, different filtering based preprocessing methods, feature extraction methods, and machine learning based classification methods, for the proper identification of steganography in the image.