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Performance Analysis of Universal Steganalysis Based on Higher Order Statistics for Neighbourhood Pixels
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Universal steganalysis of grey level JPEG images is addressed by modelling the neighbourhood relationship of the image coefficients using the higher order statistical method developed by two-step Markov Transition Probability Matrix (TPM). The implementation of TPM together with the neighbouring pixel relationship provides a better detection results as justified with the help of experimental results. The detection accuracy and execution time has been evaluated on the image sets taken from Green spun library and Google website. Performance analysis has been done using SVM, J48 and Random Forest. It is practically applicable steganalysis scheme with suitable feature dimension and with appreciable detection results with low execution time.
Keywords
Steganography, Universal Steganalysis, DCT, DWT, TPM, RF, J48, Neighbour Pixel, WEKA, SVM.
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