Detection of Stego-Images in Communication among the Terrorist Boko-Haram Sect in Nigeria

Owoeye Kolade¹, Ajayi Adedoyin Olayinka², Fadugba Sunday¹, Obayomi Adesoji¹, Isinkaye Folasade Olubusola¹

¹Ekiti State University, Ado-Ekiti, Nigeria.
²Federal University of Technology, Akure, Nigeria.

DOI: 10.4236/jdaip.2015.34017                

Abstract

Nigeria was listed as a part of terrorist states by United States of America as a result of Islamic group (Boko Haram Sect) attacks and other activities in the nation. It has also been discovered that the group employs “steganographic” schemes as a secure means for transmitting their hidden information to each other via Internet and social networks. The group has killed thousands of people since their increased insurgency in July, 2009. These challenges have affected the nation’s foreign policies, political and social economic developments. This research addresses the challenges by employing forensic technique using blind steganalysis approach to detect the presence of the hidden messages in images. Image Quality Metric is employed for extracting the features, and logistic regression is trained as the classifier to predict the stego-images. We show the effectiveness of the method by conducting test and analysis with 319 images varying in size and style. The result shows that the performance of the method is better than other steganalysis methods.

Keywords

Stego, Staganalysis, Logistic Regression, Boko-Haram Sect

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Kelley, J. (2001) Terror Groups Hide behind Web Encryption. USA Today. http://www.usatoday.com/tech/news/2001-02-05-binladen.htm
[2]	Victor, O., Waziri, A., Abraham, O., Shafi, I. and Muhammad, A. (2012) Steganography and Its Applications in Information Dessimilation on the Web Using Images as Security Embedment: A Wavelet Approach. International Journal of Computer and Information Technology, 1, 194-202.
[3]	(2006) Wikipedia. http://www.wikipedia.com/
[4]	Bateman, P. (2008) Image Steganography and Steganalysis. Department of Computing Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, 6.
[5]	Tzschoppe, R. and Bauml, R. (2003) Steganographic System Based on Higher-Order Statistics. SPIE, 2003.
[6]	Avcibas, I., Memon, N. and Sankur, B. (2001) Steganalysis Using Image Quality Metrics. Proceedings of SPIE Electronic Imaging, Security and Watermarking of Multimedia Contents, 4314, 523-531.
[7]	Avcibas, I., Sankur, B. and Memon, N. (2002) Image Steganalysis with Binary Similarity Measures. Proceedings of International Conference on Image Processing, 3, 645-648. http://dx.doi.org/10.1109/ICIP.2002.1039053
[8]	Fridrich, J. (2004) Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes. Lecture Notes in Computer Science, 3200, 67-81. http://dx.doi.org/10.1007/978-3-540-30114-1_6
[9]	Lie, W.N. and, Lin, G.S. (2005) A Feature-Based Classification Technique for Blind Image Stegananlysis. IEEE Transactions Multimeda, 7, 1007-1083. http://dx.doi.org/10.1109/TMM.2005.858377
[10]	Lyu, S.W. and Farid, H. (2006) Steganalysis Using Higher-Order Image Statistics. IEEE Transactions on Information Forensics and Security, 1, 111-119. http://dx.doi.org/10.1109/TIFS.2005.863485
[11]	Tzschoppe, R., Auml, R.B., Huber, J.B. and Kaup, A. (2003) Steganographic System Based on Higher-Order Statistics. Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents V, Santa Clara, 156-166. http://dx.doi.org/10.1117/12.477301
[12]	Johnson, N.F. and Jajodia, S. (1998) Exploring Steganography: Seeing the Unseen. Computer, 31, 26-34. http://dx.doi.org/10.1109/MC.1998.4655281
[13]	Fridrich, J., Goljan, M. and Du, R. (2001) Reliable Detection of LSB Steganography in Color and Grayscale Images. Proceedings of 2001 ACM Workshop on Multimedia and Security: New Challenges, 27-30. http://dx.doi.org/10.1145/1232454.1232466
[14]	Farid, H. (2001) Detecting Steganographic Message in Digital Images. Technical Report, TR2001-412, Dartmouth College, New Hampshire.
[15]	Xuan, G.R., Sh,i Y.Q. and Gao, J.J. (2005) Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions. Proceedings of 7th International Information Hiding Workshop, LNCS, 3727, 262-277. http://dx.doi.org/10.1007/11558859_20
[16]	Lyu, S.W. and Farid, H. (2006) Steganalysis Using Higher-Order Image Statistics. IEEE Transactions on Information Forensics and Security, 1, 111-119. http://dx.doi.org/10.1109/TIFS.2005.863485
[17]	Luo, W., Huang, F. and Huang, J. (2010) Edge Adaptive Image Steganography Based on LSB Matching Revisited. IEEE Transactions on Information Forensics and Security, 5, 201-214. http://dx.doi.org/10.1109/TIFS.2010.2041812
[18]	Sajedi, H. and Jamzad, M. (2008) A Steganalysis Method Based on Contourlet Transform Coefficients. International Conference of Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP’08), Harbin, 15-17 August, 245-248. http://dx.doi.org/10.1109/IIH-MSP.2008.11
[19]	Sajedi, H. and Jamzad, M. (2009) ContSteg: Contourlet-Based Steganography Method. Wireless Sensor Network, 1, 163-170.
[20]	Provos, N. and Honeyman, P. (2002) Detecting Steganographic Contenton the Internet. Internet Society: Network and Distributed System Security Symposium (ISOC NDSS’ February 2002), San Diego http://www.citi.umich.edu/u/provos/papers/detecting.pdf