Feature reduced blind steganalysis using DCT and spatial transform on JPEG images with and without cross validation using ensemble classifiers.

Autor: Gireeshan, M. G., Shankar, Deepa D., Azhakath, Adresya Suresh
Zdroj: Journal of Ambient Intelligence & Humanized Computing; May2021, Vol. 12 Issue 5, p5235-5244, 10p
Abstrakt: The paper discuss the outcome evaluation of JPEG images in both spatial and DCT transform and a comparative study is being done. There are four distinct steganographic algorithms—LSB matching, LSB replacement, pixel value differencing and F5 are used. The embedding performed on the images are 25% with text. The idea of cross validation is used to validate the classifier better and a comparative analysis is performed on results with and without cross validation. Features removed for investigation are the first order, second order, extended features and Markov features. Relevant features are chosen by feature reduction. This process is done using principal component analysis (PCA). This is done to eliminate redundant feature that can hamper the efficiency of classification. The classifiers used are support vector machine (SVM) and support vector machine with particle swarm optimisation (SVM-PSO). The classification is done based on six kernels like radial, dot, multiquadratic, epanechnikov and ANOVA and four sampling techniques like shuffled, linear, stratified and automatic sampling. The existing techniques had always used radial as kernel without sampling for a classification. The proposed system make use of this imperfection and has formulated the result. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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