Advances in Computer Science : an International Journal
Improving Image steganalysis performance using a graph-based feature selection method
Abstract
Steganalysis is the skill of discovering the use of steganography algorithms within an image with low or no information regarding the steganography algorithm or/and its parameters. The high-dimensionality of image data with small number of samples has presented a difficult challenge for the steganalysis task. Several methods have been presented to improve the steganalysis performance by feature selection. Feature selection, also known as variable selection, is one of the fundamental problems in the fields of machine learning, pattern recognition and statistics. The aim of feature selection is to reduce the dimensionality of image data in order to enhance the accuracy of Steganalysis task. In this paper, we have proposed a new graph-based blind steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on community detection. The experimental results show that the proposed approach is easy to be employed for steganalysis purposes. Moreover, performance of proposed method is better than several recent and well-known feature selection-based Image steganalysis methods.
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J. M. Cadenas, M. C. Garrido, and R. Martínez, "Feature subset selection Filter–Wrapper based on low quality data," Expert Systems with Applications, vol. 40, pp. 6241-6252, 2013.
A. Unler, A. Murat, and R. B. Chinnam, "mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification," Information Sciences, vol. 181, pp. 4625-4641, 2011.
Md. MonirulKabir , Md. Shahjahan, and K. Murase., "A new local search based hybrid genetic algorithm for feature selection," Neurocomputing, vol. 74, pp. 2914–2928, 2011.
Y. Liu and Y. F. Zheng, "FS_SFS: A novel feature selection method for support vector machines," Pattern Recognition, vol. 39, pp. 1333-1345, 2006.
Md. Monirul Kabir, Md. Monirul Islam, and K. Murase., "A new wrapper feature selection approach using neural network," Neurocomputing, vol. 73, pp. Pages 3273–3283, 2010.
T. M. Mitchell, "Machine Learning," McGraw-Hill,NewYork., 1997.
"Filler, T., Pevný, T., Bas, P., 2010. Break our steganographic system (BOSS). http://exile.felk.cvut.cz/boss/〉, (accessed 21.09.12)."
"Lubenko, I., Ker, A.D., 2012. Going from small to large data in steganalysis. In: Media Lubenko, I., Ker, A.D., 2012. Going from small to large data in steganalysis. In: Media national Society for Optical Engineering, vol. 8303, pp. 0M01–0M10.."
"Pevný, T., Ker, A.D. 2013. The Challenges of Rich Features in Universal Steganalysis. In: Proceedings of SPIE - The International Society for Optical Engineering 8665, 86650M–86650M–15.."
"Ker, A.D., Pevný, T., 2014. A mishmash of methods for mitigating the model mismatch mess. In: Proceedings of SPIE – The International Society for Optical Engineering 9028, 90280I–90280I–15."
B. Bonev, F. Escolano, D. Giorgi, and S. Biasotti, "Information-theoretic selection of high-dimensional spectral features for structural recognition," Computer Vision and Image Understanding, vol. 117, pp. 214-228, 2013.
Ahmed K. Farahat, Ali Ghodsi, and M. S. Kamel, "Efficient greedy feature selection for unsupervised learning," Knowledge and Information Systems vol. 35, pp. 285-310, 2013.
M. H. Aghdam, N. Ghasem-Aghaee, and M. E. Basiri, "Text feature selection using ant colony optimization," Expert Systems with Applications, vol. 36, pp. 6843-6853, 2009.
P. Shamsinejadbabki and M. Saraee, "A new unsupervised feature selection method for text clustering based on genetic algorithms," Journal of Intelligent Information Systems, vol. 38, pp. 669-684, 2011.
H. Uğuz, "A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm," Knowledge-Based Systems, vol. 24, pp. 1024-1032, 2011.
L. D. Vignolo, D. H. Milone, and J. Scharcanski, "Feature selection for face recognition based on multi-objective evolutionary wrappers," Expert Systems with Applications, vol. 40, pp. 5077-5084, 2013.
L. Zini, N. Noceti, G. Fusco, and F. Odone, "Structured multi-class feature selection with an application to face recognition," Pattern Recognition Letters, 2014.
S. F. da Silva, M. X. Ribeiro, J. d. E. S. Batista Neto, C. Traina-Jr, and A. J. M. Traina, "Improving the ranking quality of medical image retrieval using a genetic feature selection method," Decision Support Systems, vol. 51, pp. 810-820, 2011.
E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "A simultaneous feature adaptation and feature selection method for content-based image retrieval systems," Knowledge-Based Systems, vol. 39, pp. 85-94, 2013.
H. H. Inbarani, A. T. Azar, and G. Jothi, "Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis," Comput Methods Programs Biomed, vol. 113, pp. 175-85, 2014.
P. Jaganathan and R. Kuppuchamy, "A threshold fuzzy entropy based feature selection for medical database classification," Comput Biol Med, vol. 43, pp. 2222-9, Dec 2013.
Y. Chen and D. Liginlal, "A maximum entropy approach to feature selection in knowledge-based authentication," Decision Support Systems, vol. 46, pp. 388-398, 2008.
"J. Fridrich, "Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes," in Information Hiding, 2005.."
Y. Saeys, I. Inza, and P. Larranaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, pp. 2507-17, Oct 1 2007.
G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, pp. 16-28, 2014.
H. Liu and L. Yu, "Toward Integrating Feature Selection Algorithms for Classification and Clustering," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 17, pp. 491 - 502 2005.
Lei Yu and H. Liu., "Feature selection for high-dimensional data: A fast correlation-based filter solution," In:Proceedings of the 20th International Conference on Machine Learning, pp. pp. 856–863, 2003.
S. Theodoridis and C. Koutroumbas, "Pattern Recognition, 4th Edn," Elsevier Inc, 2009.
Xiaofei He, Deng Cai, and P. Niyogi1, "Laplacian Score for Feature Selection," Adv. Neural Inf. Process. Syst, vol. 18, pp. 507-514, 2005.
Quanquan Gu, Zhenhui Li, and J. Han, "Generalized Fisher Score for Feature Selection," In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 2011.
Artur J. Ferreira and M. A. T. Figueiredo, "An unsupervised approach to feature discretization and selection," Pattern Recognition, vol. 45, pp. 3048–3060, 2012.
S. Tabakhi, P. Moradi, and F. Akhlaghian, "An unsupervised feature selection algorithm based on ant colony optimization," Engineering Applications of Artificial Intelligence, vol. 32, pp. 112-123, 2014.
Hui-Huang Hsu, Cheng-Wei Hsieh, and M.-D. Lu, "Hybrid feature selection by combining filters and wrappers," Expert Systems with Applications, vol. 38, pp. 8144–8150, 2011.
R. Battiti, "Using mutual information for selecting features in supervised neural net learning," Neural Networks, IEEE Transactions on, vol. 5, pp. pp. 537 - 550 1994.
R. Archibald and G. Fann, "Feature Selection and Classification of Hyperspectral Images With Support Vector Machines," Geoscience and Remote Sensing Letters, IEEE, vol. 4, pp. pp. 674 - 677 2007.
M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," Neural Inform. Process. Systems 1, pp. 585-592, 2002.
J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Machine Intell, vol. 22, pp. 888–905, 2000.
F. Chung, "Spectral Graph Theory," In: Regional Conference Series in Mathematics American Mathematical Society, vol. 92, pp. 1-212, 1997.
Jung-Yi Jiang, Ren-Jia Liou, and S.-J. Lee, "A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification," IEEE Transactions Knowledge and Data Engineering, vol. 23, pp. 335 - 349 2011.
Xi Zhao, W. Deng., and Y. Sh, "Feature Selection with Attributes Clustering by Maximal Information Coefficient " Procedia Computer Science, vol. 17, 2013.
Chuan Shi, Yanan Cai , Di Fu , Yuxiao Dong , and B. Wu, "A link clustering based overlapping community detection algorithm," Data & Knowledge Engineering, vol. 87, pp. Pages 394–404, 2013.
Yakun Li , Hongzhi Wang, Jianzhong Li , and H. Gao, "Efficient community detection with additive constrains on large networks," Knowledge-Based Systems, vol. 52, pp. Pages 268–278, 2013.
Yawen Jiang , Caiyan Jia, and J. Yu, "An efficient community detection method based on rank centrality," Physica A, vol. 392, pp. Pages 2182–2194, 2013.
J. G. V. Blondel, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, vol. 10008, pp. pp. 1–12, 2008.
A.A. Hagberg, D.A. Schult, and P. J. Swart, "Exploring network structure, dynamics, and function using NetworkX," in: Proceedings of the 7th Python in Science Conference (SciPy2008), Pasadena, CA, USA, pp. pp. 11–15, 2008.
M. Bastian, et al.,, "Gephi: an open source software for exploring and manipulating networks.," In: International AAAI Conference on Weblogs and Social Media, North America,, 2009.
Hanchuan Peng, Fuhui Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 27, pp. 1226 - 1238 2005.
"Bas, P. Filler, T. Pevn’{y}, T., 2011. Information hiding. In: 13th International Workshop. Springer-Verlag, New York."
