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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

Bashkirova, D. (2016). Convolutional neural networks for image steganalysis. BioNanoScience, 6(3), 246-248.

Bera, S., et al. (2018). Performance Analysis of Universal Steganalysis Based on Higher Order Statistics for Neighbourhood Pixels in Coimbatore Institute of Information Technology, CiiT International Journal of Fuzzy System, 10(4), 85-91.

Binghamton, retrieved Oct 4, 2020 from http://dde.binghamton,edu/download.edu/download/syndrome.

Chen, B., et al. (2014). Mixing high-dimensional features for JPEG steganalysis with ensemble classifier. Signal, Image and Video Processing, 8(8), 1475-1482.

Chhikara, R. R., et al. (2018). An improved dynamic discrete firefly algorithm for blind image steganalysis. International Journal of Machine Learning and Cybernetics, 9(5), 821-835.

Clausi, D. A., et al. (2005). Design-based texture feature fusion using Gabor filters and co-occurrence probabilities. IEEE Transactions on Image Processing, 14(7), 925-936.

Denemark, T. D., et al. (2016). Steganalysis features for content-adaptive JPEG steganography. IEEE Transactions on Information Forensics and Security, 11(8), 1736-1746.

 Filler, T., et al. (2011). Design of adaptive steganographic schemes for digital images. In Media Watermarking, Security, and Forensics III. International Society for Optics and Photonics. (Vol. 7880, p. 78800F).

Fridrich, J. (2004). Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In International Workshop on Information Hiding. Springer, Berlin, Heidelberg, 67-81.

Fridrich, J., et al. (2004). Perturbed quantization steganography with wet paper codes. In Proceedings of the 2004 workshop on Multimedia and security, 4-15.

Fridrich, J., et al. (2007,). Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. In Proceedings of the 9th workshop on Multimedia & security, 3-14.

Gul, G., et al. (2013). JPEG image steganalysis using multivariate PDF estimates with MRF cliques. IEEE transactions on information forensics and security, 8(3), 578-587.

Guo, L., et al. (2014). Uniform embedding for efficient JPEG steganography. IEEE transactions on Information Forensics and Security, 9(5), 814-825.

Holub, V., et al. (2013). Digital image steganography using universal distortion. In Proceedings of the first ACM workshop on Information hiding and multimedia security, 59-68.

Holub, V., et al. (2014). Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Transactions on Information Forensics and Security, 10(2), 219-228.

Holub, V., et al. (2015). Phase-aware projection model for steganalysis of JPEG images. In Media Watermarking, Security, and Forensics 2015 (Vol. 9409, p. 94090T). International Society for Optics and Photonics.

JPG steganography, retrieved Sept. 29,2020 from  ihttp://www.guillermito2.net/stegano/jsteg.

Karimi, H., et al. (2015). Steganalysis of JPEG images using enhanced neighbouring joint density features. IET Image Processing, 9(7), 545-552.

Kumari, M., et al. (2017). Blind image steganalysis using neural networks and wrapper feature selection. In 2017 International Conference on Computing, Communication and Automation (ICCCA), 1065-1069.

Kodovský, J., et al. (2012). Steganalysis of JPEG images using rich models. In Media Watermarking, Security, and Forensics 2012). International Society for Optics and Photonics, 8303, 83030A.

Laimeche, L., et al. (2018). A new feature extraction scheme in wavelet transform for stego image classification. Evolving Systems, 9(3), 181-194.

Liu, F., et al. (2019). Feature selection for image steganalysis using binary bat algorithm. IEEE Access, 8, 4244-4249.

Lyu, S., et al. (2002). Detecting hidden messages using higher-order statistics and support vector machines. In International Workshop on information hiding. Springer, Berlin, Heidelberg, 340-354.

Mohammadi, F. G., et al. (2014). Image steganalysis using a bee colony based feature selection algorithm. Engineering Applications of Artificial Intelligence, 31, 35-43.

Outgues, retrieved Oct.3,2020 from http://www.outgues.org.

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