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Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks

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Abstract

Steganography is the science of hiding information in a media such as video, image or audio files. On the other hand, the aim of steganalysis is to detect the presence of embedded data in a given media. In this paper, a steganalysis method is presented for the colored joint photographic experts group images in which the statistical moments of contourlet transform coefficients are used as the features. In this way, binary particle swarm optimization algorithm is also employed as a closed-loop feature selection method to select the efficient features in tandem with improvement of the detection rate. Nonlinear support vector machine and two variants of radial basis neural networks, i.e., radial basis function and probabilistic neural network, are used as the classification tools and their performance is compared in detecting the stego and clean images. Experimental results show that even for low embedding rates, the detection accuracy of the proposed method is more than 80% along with 30% reduction in the size of feature set.

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References

  1. Petitcolas FAP, Anderson RJ, Kuhn MG (1999) Information hiding-a survey. Proc IEEE (special issue on protection of multimedia content) 87:1062–1078

    Google Scholar 

  2. Sharp T (2001) An implementation of key-based digital signal steganography. In: The proceedings of the 4th international workshop on information hiding, vol 2137 of Springer LNCS, pp 13–26

  3. Zhang T, Li W, Zhang Y, Zheng E, Ping X (2010) Steganalysis of LSB matching based on statistical modeling of pixel difference distributions. Inf Sci 180:4685–4694

    Article  Google Scholar 

  4. Westfeld A (2001) F5- a steganographic algorithm: high capacity despite better steganalysis. In: The proceedings of the 4th international workshop on information hiding, vol 2137 of Springer LNCS, pp 289–302

  5. Provos N (2001) Defending against statistical steganalysis. In: The proceedings of the 10th USENIX security symposium, pp 24–36

  6. Provos J, Goljan M, Du R (2001) Detecting LSB steganography in color and gray-scale images. Multimedia IEEE 8:22–28

    Google Scholar 

  7. Hetzl S (2003) Steghide Software http://steghide.sourceforge.net/ Accessed 28 Dec 2009

  8. Solanki K, Sarkar A, Manjunath BS (2008) YASS: yet another steganographic scheme that resists blind steganalysis. In: The proceedings of the 9th international workshop on information hiding, vol 4567 of Springer LNCS, pp 16–31

  9. Wang H, Wang S (2004) Cyber warfare: steganography vs. steganalysis. Commun ACM 47:76–82

    Article  Google Scholar 

  10. Nissar A, Mir AH (2010) Classification of steganalysis techniques: a study. Digit Signal Process 20:1758–1770

    Article  Google Scholar 

  11. Shi Y, Chen C, Chen W (2006) A Markov process based approach to effective attacking JPEG steganography. In: The proceedings of the 8th international workshop on information hiding, pp 249–264

  12. Wang Y, Moulin P (2007) Optimized feature extraction for learning-based image steganalysis. IEEE Trans Inform Forensics Secur 2:31–45

    Article  Google Scholar 

  13. Pevny T, Fridrich J (2007) Merging Markov and DCT features for multiclass JPEG steganalysis. SPIE-IS & T Electronic Imaging 650503:1–13

  14. Fridrich J, Goljan M, Du R (2001) Reliable detection of LSB steganography in color and grayscale images. In: The proceedings of the ACM workshop on multimedia security, pp 27–30

  15. Goljan M, Fridrich J, Holotyak T (2006) New blind steganalysis and its implications. In: The proceedings of the SPIE 6072, pp 1–13

  16. Ker AD, Lubenko I (2009) Feature reduction and payload location with WAM steganalysis. In: The proceedings of the SPIE 7254, pp 0A01–0A13

  17. Luo XY, Wang DS, Wang P, Liu FL (2008) A review on blind detection for image steganography. Signal Process 88:2138–2157

    Article  MATH  Google Scholar 

  18. Sajedi H, Jamzad M (2010) CBS: contourlet-based steganalysis method. J Signal Process Syst 61:367–373

    Article  Google Scholar 

  19. Po DDY, Do MN (2006) Directional multiscale modeling of images using the contourlet transform. IEEE Trans Image Process 15:1610–1620

    Article  MathSciNet  Google Scholar 

  20. Sheikhan M, Mohammadi N (2011) Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput Appl (Published online: 1 May 2011, doi:10.1007/s00521-011-0599-1)

  21. Lee S, Soak S, Oh S, Pedrycz W, Jeon M (2008) Modified binary particle swarm optimization. Progress Natural Sci 18:1161–1166

    Article  MathSciNet  Google Scholar 

  22. Lie WN, Lin GS (2005) A feature-based classification technique for blind image steganalysis. IEEE Trans Multimedia 7:1007–1020

    Article  Google Scholar 

  23. Xuan GR, Shi YQ, Gao JJ, Zou DK, Yang CY, Zhang ZP, Chai PQ, Chen CH, Chen W (2005) Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: The proceedings of the 7th international information hiding workshop, vol 3727 of Springer LNCS, pp 262–277

  24. Zhou Z, Hui M (2009) Steganalysis for Markov feature of difference array in DCT domain. In: The proceedings of the 6th international conference on fuzzy systems and knowledge discovery, vol 7, pp 581–584

  25. Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inform Forensics Secur 1:111–119

    Article  Google Scholar 

  26. Chamorro AGH, Miyatake MN (2010) A new methodology of image steganalysis including for JPEG steganography. In: The proceedings of the international conference on electronics, robotics and automotive mechanics, pp 434–438

  27. Lin J-Q, Zhong S-P (2009) JPEG image steganalysis method based on binary similarity measures. In: The proceedings of the international conference on machine learning and cybernetics, vol 4, pp 2238–2243

  28. Bhat VH, Krishna S, Shenoy PD, Venugopal KR, Patnaik LM (2010) HUBFIRE-A multi-class SVM based JPEG steganalysis using HBCL statistics and FR index. In: The proceedings of the international conference on security and cryptography, pp 1–6

  29. Yi X, Wang YA (2009) An investigation of genetic algorithm on steganalysis techniques. In: The proceedings of the 5th international conference on intelligent information hiding and multimedia signal processing, pp 1118–1121

  30. Zhi-Min He Ng WWY, Chan PPK, Yeung DS (2010) JPEG steganalysis based on class-wise non-principal components analysis and multi-directional Markov model. In: The proceedings of the international conference on machine learning and cybernetics, vol 1, pp 500–503

  31. Cho S, Wang J, Kuo C-CJ, Cha B-H (2010) Block-based image steganalysis for a multi-classifier. In: The proceedings of the international conference on multimedia and expo, pp 1457–1462

  32. Bayram S, Dirik AE, Sencar HT, Memon N (2010) An ensemble of classifiers approach to steganalysis. In: The proceedings of the 20th international conference on pattern recognition, pp 4376–4379

  33. Asadi N, Jamzad M, Sajedi H (2008) Improvements of image-steganalysis using boosted combinatorial classifiers and Gaussian high pass filtering. In: The proceedings of the international conference on intelligent information hiding and multimedia signal processing, pp 1508–1511

  34. Luo P, Su Y (2010) Research on simulated annealing clustering algorithm in the steganalysis of image based on the one-class support vector machine. In: The proceedings of the international conference on computer application and system modeling, vol 14, pp 446–450

  35. Wang Y, Liu J, Zhang W, Lian S (2010) Reliable JPEG steganalysis based on multi-directional correlations. Signal Process Image Commun 25:577–587

    Article  Google Scholar 

  36. Sabeti V, Samavi S, Mahdavi M, Shirani S (2010) Steganalysis and payload estimation of embedding in pixel differences using neural networks. Pattern Recogn 43:405–415

    Article  MATH  Google Scholar 

  37. Liu Q, Sung AH, Qiao M, Chen Z, Ribeiro B (2010) An improved approach to steganalysis of JPEG images. Inf Sci 180:1643–1655

    Article  Google Scholar 

  38. Liu Q, Sung AH, Chen Z, Xu J (2008) Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images. Pattern Recogn 41:56–66

    Article  MATH  Google Scholar 

  39. Raval MS (2009) A secure steganographic technique for blind steganalysis resistance. In: The proceedings of the 7th international conference on advances in pattern recognition, pp 25–28

  40. Wahab AW, Briffa JA, Schaathun HG, Ho ATS (2009) Conditional probability based steganalysis for JPEG steganography. In: The proceedings of the international conference on signal processing systems, pp 205–209

  41. Yamini B, Sabitha R (2010) Steganalytic attack for an adaptive steganography using support vector machine. In: The proceedings of the international conference on emerging trends in robotics and communication technologies, pp 56–58

  42. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14:2091–2106

    Article  MathSciNet  Google Scholar 

  43. Zhao Q, Cao J, Hu Y (2009) 3-Joint optimization of feature selection and parameters for multi-class SVM in skin symptomatic recognition. In: The proceedings of the international conference on artificial intelligence and computational intelligence, vol 1, pp 407–411

  44. Tian J, Li M, Chen F (2010) Dual-population based coevolutionary algorithm for designing RBFNN with feature selection. Expert Syst Appl 37:6904–6918

    Article  Google Scholar 

  45. Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer, Boston

    Book  MATH  Google Scholar 

  46. Perez CA, Cament LA, Castillo LE (2011) Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count. Pattern Recogn 44:951–963

    Article  Google Scholar 

  47. Gurwicz Y, Yehezkel R, Lachover B (2011) Multiclass object classification for real-time video surveillance systems. Pattern Recogn Lett 32:805–815

    Article  Google Scholar 

  48. Tian D, Zeng X, Keane J (2011) Core-generating approximate minimum entropy discretization for rough set feature selection in pattern classification. Int J Approx Reason 52:863–880

    Article  Google Scholar 

  49. Bontempi G (2007) A blocking strategy to improve gene selection for classification of gene expression data. IEEE/ACM Trans Comput Biol Bioinform 4:293–300

    Article  Google Scholar 

  50. Chang C-Y, Chen S-J, Tsai M-F (2010) Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images. Pattern Recogn 43:3494–3506

    Article  MATH  Google Scholar 

  51. Lim CP, Wang SL, Tan KS, Navarro J, Jain LC (2010) Use of the circle segments visualization technique for neural network feature selection and analysis. Neurocomputing 73:613–621

    Article  Google Scholar 

  52. Cheng HD, Shan J, Ju W, Guo Y, Zhang L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn 43:299–317

    Article  MATH  Google Scholar 

  53. Tsang C-H, Kwong S, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recogn 40:2373–2391

    Article  MATH  Google Scholar 

  54. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2011) Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst Appl 38:5947–5957

    Article  Google Scholar 

  55. Hua J, Tembe WD, Dougherty ER (2009) Performance of feature selection methods in the classification of high-dimension data. Pattern Recogn 42:409–424

    Article  MATH  Google Scholar 

  56. Heikkinen V, Tokola T, Parkkinen J, Korpela I, Jaaskelainen T (2010) Simulated multispectral imagery for tree species classification using support vector machines. IEEE Trans Geosci Remote Sens 48:1355–1364

    Article  Google Scholar 

  57. Puig D, Angel Garcia M, Melendez J (2010) Application-independent feature selection for texture classification. Pattern Recogn 43:3282–3297

    Article  MATH  Google Scholar 

  58. Ruvolo P, Fasel I, Movellan JR (2010) A learning approach to hierarchical feature selection and aggregation for audio classification. Pattern Recogn Lett 31:1535–1542

    Article  Google Scholar 

  59. Tan KC, Teoh EJ, Yu Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36:8616–8630

    Article  Google Scholar 

  60. Casale S, Russo A, Serrano S (2007) Multistyle classification of speech under stress using feature subset selection based on genetic algorithms. Speech Commun 49:801–810

    Article  Google Scholar 

  61. Gharavian D, Sheikhan M, Nazerieh AR, Garoucy S (2011) Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network. Neural Comput Appl (Published online: 28 May 2011, doi:10.1007/s00521-011-0643-1)

  62. Wang S, Li D, Song X, Wei Y, Li H (2011) A feature selection method based on improved Fisher’s discriminant ratio for text sentiment classification. Expert Syst Appl 38:8696–8702

    Article  Google Scholar 

  63. Catal C, Diri B (2009) Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Inf Sci 179:1040–1058

    Article  Google Scholar 

  64. Zhao H, Sinha AP, Ge W (2009) Effects of feature construction on classification performance: an empirical study in bank failure prediction. Expert Syst Appl 36:2633–2644

    Article  Google Scholar 

  65. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: The proceedings of the international conference on systems, man and cybernetics, pp 4104–4108

  66. Babaoglu I, Findik O, Ülker E (2010) A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst Appl 37:3177–3183

    Article  Google Scholar 

  67. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm) Accessed 7 Mar 2010

  68. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: The proceedings of the 5th annual ACM workshop on COLT, pp 144–152

  69. Shihong Y, Ping L, Peiyi H (2003) SVM classification: its content and challenges. Appl Math J Chinese Univ Ser B 18:332–342

    Article  MathSciNet  MATH  Google Scholar 

  70. Moody J (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:281–294

    Article  Google Scholar 

  71. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York

    MATH  Google Scholar 

  72. Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118

    Article  Google Scholar 

  73. Schaefer G, Stich M (2004) UCID: An uncompressed colour image database. Proc. SPIE, Storage and Retrieval Methods and Application for Multimedia, San Jose, CA 427–480 (http://vision.cs.aston.ac.uk/datasets/UCID/ucid.html) Accessed 20 Nov 2009

  74. Upham D, Jsteg. Software (ftp://ftp.funet.fi/pub/crypt/steganography) Accessed 6 May 2009

  75. Provos N, Outguess Software (www.outguess.org) Accessed 3 Dec 2009

  76. Sallee P, Model-Based Steganography (http:\\www.philsallee.com\mbsteg\index.html) Accessed 3 Dec 2009

  77. Latham A, JPHS software (http://linux01.gwdg.de/~alatham/stego.html) Accessed 3 Dec 2009

  78. Liu Q, Sung AH, Ribeiro B, Wei M, Chen Z, Xu J (2008) Image complexity and feature mining for steganalysis of least significant bit matching steganography. Inf Sci 178:21–36

    Article  Google Scholar 

  79. Geetha S, Sivatha Sindhu SS, Kamaraj N (2009) Blind image steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system. Comput Secur 28:683–697

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, EE Department, Islamic Azad University, South Tehran Branch, P.O. Box 11365-4435, Tehran, Iran

    Mansour Sheikhan & Mansoureh Pezhmanpour

  2. Multimedia Systems Group, IT Department, Iran Telecom Research Center, Tehran, Iran

    M. Shahram Moin

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  1. Mansour Sheikhan
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  2. Mansoureh Pezhmanpour
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  3. M. Shahram Moin
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Correspondence to Mansour Sheikhan.

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Sheikhan, M., Pezhmanpour, M. & Moin, M.S. Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks. Neural Comput & Applic 21, 1717–1728 (2012). https://doi.org/10.1007/s00521-011-0729-9

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