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Author(s): S. Bera, K. Thakur, P. Vyas, .M.Thakur, A. Shrivastava

Email(s): prafullavyas@gmail.com

Address: VYTPG College, Durg
Professor, PTRSU, Raipur
Asst. Professor, Disha College, Raipur
Developer Associate, SAP Labs Pvt. Ltd, Banglore
Asst. Professor, GEC, Jagdalpur

Published In:   Volume - 34,      Issue - 1,     Year - 2021

DOI: 10.52228/JRUB.2021-34-1-3  

Universal isteganalysis 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 and comparable detection results. The detection accuracy is evaluated on the stego image database using eXtreme Gradient Boosting (XGBoost) with Principal Component Analysis (PCA) on nsF5 and JUNIWARD hiding techniques. Execution time is also compared for all the classifiers. The images are taken from Green spun library and Google website- eXtreme Gradient Boosting.

Cite this article:
Bera et al. (2021). Higher Order Statistics Based Blind Steg analysis using Deep Learning. Journal of Ravishankar University (Part-B: Science), 34(1), pp. 19-28.DOI: https://doi.org/10.52228/JRUB.2021-34-1-3

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