Image steganalysis using Artificial Bee Colony algorithm

Abstract Steganography is the science of secure communication where the presence of the communication cannot be detected while steganalysis is the art of discovering the existence of the secret communication. Processing a huge amount of information takes extensive execution time and computational sources most of the time. As a result, it is needed to employ a phase of preprocessing, which can moderate the execution time and computational sources. In this paper, we propose a new feature-based blind steganalysis method for detecting stego images from the cover (clean) images with JPEG format. In this regard, we present a feature selection technique based on an improved Artificial Bee Colony (ABC). ABC algorithm is inspired by honeybees’ social behaviour in their search for perfect food sources. In the proposed method, classifier performance and the dimension of the selected feature vector depend on using wrapper-based methods. The experiments are performed using two large data-sets of JPEG images. Experimental results demonstrate the effectiveness of the proposed steganalysis technique compared to the other existing techniques.

[1]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[2]  Xiaoming Xu,et al.  A hybrid genetic algorithm for feature selection wrapper based on mutual information , 2007, Pattern Recognit. Lett..

[3]  Magdalena Metlicka,et al.  Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems , 2015, Swarm Evol. Comput..

[4]  Yun Q. Shi,et al.  JPEG image steganalysis utilizing both intrablock and interblock correlations , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[5]  Jessica J. Fridrich,et al.  Steganalysis in high dimensions: fusing classifiers built on random subspaces , 2011, Electronic Imaging.

[6]  Jongmin Yoon,et al.  Performance comparison of several feature selection methods based on node pruning in handwritten character recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[7]  Gilbert L. Peterson,et al.  A new blind method for detecting novel steganography , 2005, Digit. Investig..

[8]  Nasser Ghasem-Aghaee,et al.  Text feature selection using ant colony optimization , 2009, Expert Syst. Appl..

[9]  Ouen Pinngern,et al.  Feature subset selection wrapper based on mutual information and rough sets , 2012, Expert Syst. Appl..

[10]  Satoru Miyano,et al.  A Top-r Feature Selection Algorithm for Microarray Gene Expression Data , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  Hedieh Sajedi,et al.  Using contourlet transform and cover selection for secure steganography , 2010, International Journal of Information Security.

[12]  Luiz Eduardo Soares de Oliveira,et al.  A Methodology for Feature Selection Using Multiobjective Genetic Algorithms for Handwritten Digit String Recognition , 2003, Int. J. Pattern Recognit. Artif. Intell..

[13]  Hedieh Sajedi,et al.  Steganalysis based on steganography pattern discovery , 2016, J. Inf. Secur. Appl..

[14]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[15]  Zexuan Zhu,et al.  Markov blanket-embedded genetic algorithm for gene selection , 2007, Pattern Recognit..

[16]  M. Kugler,et al.  Feature subset selection for support vector machines using confident margin , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[17]  Lei Liu,et al.  Feature Selection Using Mutual Information: An Experimental Study , 2008, PRICAI.

[18]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[19]  D. Andina,et al.  Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[20]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[21]  Mohammad Saniee Abadeh,et al.  A Survey of Data Mining Techniques for Steganalysis , 2012 .

[22]  Hedieh Sajedi,et al.  A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring , 2015 .

[23]  Kamel Mohamed Faraoun,et al.  Data dimensionality reduction based on genetic selection of feature subsets , 2015 .

[24]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[25]  Jessica J. Fridrich,et al.  Calibration revisited , 2009, MM&Sec '09.

[26]  Cheng-Hong Yang,et al.  Dimensionality Reduction using GA-PSO , 2006, JCIS.

[27]  Claudio De Stefano,et al.  A GA-based feature selection approach with an application to handwritten character recognition , 2014, Pattern Recognit. Lett..

[28]  G. Kim,et al.  FEATURE SELECTION USING GENETIC ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION , 2004 .

[29]  Sumaiya Iqbal,et al.  Solving the multi-objective Vehicle Routing Problem with Soft Time Windows with the help of bees , 2015, Swarm Evol. Comput..

[30]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[31]  Jian-Bo Yang,et al.  Feature Selection Using Probabilistic Prediction of Support Vector Regression , 2011, IEEE Transactions on Neural Networks.

[32]  Tomás Pevný,et al.  Merging Markov and DCT features for multi-class JPEG steganalysis , 2007, Electronic Imaging.

[33]  Lluís A. Belanche Muñoz,et al.  Feature selection algorithms: a survey and experimental evaluation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[34]  Hélio Pedrini,et al.  Data feature selection based on Artificial Bee Colony algorithm , 2013, EURASIP J. Image Video Process..

[35]  Amer Draa,et al.  An artificial bee colony algorithm for image contrast enhancement , 2014, Swarm Evol. Comput..

[36]  Jessica J. Fridrich,et al.  Forensic steganalysis: determining the stego key in spatial domain steganography , 2005, IS&T/SPIE Electronic Imaging.

[37]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[38]  Hedieh Sajedi,et al.  BSS: Boosted steganography scheme with cover image preprocessing , 2010, Expert Syst. Appl..

[39]  R. R. Rajalaxmi,et al.  Feature selection using Artificial Bee Colony for cardiovascular disease classification , 2014, 2014 International Conference on Electronics and Communication Systems (ICECS).

[40]  N. Kamaraj,et al.  Optimized Image Steganalysis through Feature Selection using MBEGA , 2010, ArXiv.

[41]  Huaiqing Wang,et al.  A discretization algorithm based on a heterogeneity criterion , 2005, IEEE Transactions on Knowledge and Data Engineering.

[42]  Tomás Pevný,et al.  Steganalysis by Subtractive Pixel Adjacency Matrix , 2009, IEEE Transactions on Information Forensics and Security.

[43]  Hao Gao,et al.  An improved artificial bee colony and its application , 2016, Knowl. Based Syst..

[44]  Dong Xu,et al.  A local information-based feature-selection algorithm for data regression , 2013, Pattern Recognit..

[45]  Yang Cao,et al.  An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem , 2019, 2019 Chinese Control And Decision Conference (CCDC).

[46]  Salim Chikhi,et al.  Artificial bees for multilevel thresholding of iris images , 2015, Swarm Evol. Comput..

[47]  T. SUMATHI,et al.  ARTIFICIAL BEE COLONY OPTIMIZATION FOR FEATURE SELECTION IN OPINION MINING , 2014 .

[48]  Jacek M. Zurada,et al.  Normalized Mutual Information Feature Selection , 2009, IEEE Transactions on Neural Networks.

[49]  Hedieh Sajedi,et al.  Secure steganography based on embedding capacity , 2009, International Journal of Information Security.

[50]  Qingzhong Liu,et al.  Steganalysis of DCT-embedding based adaptive steganography and YASS , 2011, MM&Sec '11.