Abstract
Steganalysis is the opposite art to steganography, whose goal is to detect whether or not the seemly innocent objects like image hiding message. Image steganalysis is important research issue of information security field. With the development of steganography technology, steganalysis becomes more and more difficult. Regarding the problem of improving the performance of image steganalysis, many research work have been done. Based on current research, large scale training set will be the feasible means to improve the steganalysis performance. Classic classifier is out of work to deal with large scale images steganalysis. In this paper, a parallel Support Vector Machines based on MapReduce is used to build the steganalysis classifier according to large scale training samples. The efficiency of the proposed method is illustrated with an experiment analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chanu, Y.J., Tuithung, T., Manglem, S.K.: A short survey on image steganography and steganalysis techniques. In: 3rd National Conference on Emerging Trends and Applications in Computer Science, pp. 52–55 (2012)
Bilal, I., Roj, M.S., Kumar, R., Mishra, P.K.: Recent advancement in audio steganography. In: 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 402–405 (2014)
Mazurczyk, W., Caviglione, L.: Steganography in modern smartphones and mitigation techniques. IEEE Commun. Surv. Tutorials 17(1), 334–357 (2015)
Khosravirad, S.R., Eghlidos, T., Ghaemmaghami, S.: Closure of sets: a statistically hypersensitive system for steganalysis of least significant bit embedding. IET Signal Process. 5(4), 379–389 (2011)
Tang, M.W., Fan, M.Y., Wang, G.W.: An extential steganalysis of information hiding for F5. In: 2nd International Workshop on Intelligent Systems and Applications, pp. 1–4 (2010)
Zhang, Z., Hu, D.H., Yang, Y., Su, B.: A universal digital image steganalysis method based on sparse representation. In: 9th International Conference on Computational Intelligence and Security, pp. 437–441 (2013)
Gul, G., Kurugollu, F.: SVD-based universal spatial domain image steganalysis. IEEE Trans. Inf. Forensics Secur. 5(2), 349–353 (2010)
Davidson, J., Jalan, J.: Steganalysis using partially ordered markov models. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 118–132. Springer, Heidelberg (2010)
Dai, Z.H., Xiong, Q., Peng, Y., Gao, H.H.: Research on the large scale image steganalysis technology based on cloud computing and BP neutral network. In: The 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 415–419 (2012)
Fox, G.C., Bae, S.H.: Parallel data mining from multicore to cloudy grids. In: High Performance Computing and Grids workshop, pp. 311–340 (2008)
Ekanayake, J., Li, H.: Twister: a runtime for iterative MapReduce. In: The First International Workshop on MapReduce and Its Applications of ACM HPDC, pp. 810–818 (2010)
Islam, N.S., Wasi-ur-Rahman, M., Lu, X.Y., Shankar, D., Panda, D.K.: Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters. In: IEEE International Conference on Big Data, pp. 243–252 (2015)
Huang, C., Gao, J.J., Xuan, G.R., Shi, Y.Q.: Steganalysis based on moments in the frequency domain of wavelet sub- band’s histogram for JPEG images. Comput. Eng. Appl. 30, 95–97 (2006)
Acknowledgments
This work is supported by the national science foundation (No. 61472230), National Natural Science Foundation of China (Grant No. 61402271), the Natural Science Foundation of Shandong Province (Grant No. ZR2015JL023 and Grant No. ZR2015FL025), Shandong science and technology development plan (Grant No. J15LN54), Key R & D program in Shandong Province (Grant No. 2015GGX101012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sun, Z., Huang, H., Li, F. (2016). Large Scale Image Steganalysis Based on MapReduce. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-40663-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40662-6
Online ISBN: 978-3-319-40663-3
eBook Packages: Computer ScienceComputer Science (R0)