Abstract
Steganalysis is an important extension to existing security infrastructure, and is gaining more research focus of forensic investigators and information security researchers. This paper reports the design principles and evaluation results of a new experimental blind image steganalysing system. This work approaches the steganalysis task as a pattern classification problem. The detection accuracy of the steganalyser depends on the selection of low-dimensional informative features. We investigate this problem as a three step process and propose a novel steganalyser with the following implications: a) Selection of the Curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, than other conventional wavelet transforms. b) Exploiting the empirical moments of the transformation as effective steganalytic features c) Realizing the system using an efficient classifier, evolutionary-Support Vector Machine (SVM) model that provides promising classification rate. An extensive empirical evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.
Similar content being viewed by others
References
Avcibas I, Memon N, Sankur B (2003) Steganalysis using image quality metrics. IEEE Trans Image Process 12(2):221–229
Avcibas I, Memon N, kharrazi M, Sankur B (2005) Image steganalysis with binary similarity measures. J EURASIP, Appl Signal Process, Hindawi
Baker JE (1985) Adaptive selection methods for genetic algorithms. In Proc. 1st Int’l Conf. On Genetic Algorithms. Pg 101–111
Bánoci V, Broda M, Bugár G, Levický D (2014) Universal image steganalytic method. Radio Eng 23(4):1213–1220
Bender W, Gruhl D, Morimot N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3/4):313–336
Brown A. S-tools version 4.0. [Online]. Available: http://members.tripod.com/steganography/stego/s-tools4.html
Cachin C (1998) An information theoretic model for steganography, Lecture Notes in Computer Science: 2nd Int’l Workshop on Information Hiding 1525, pp. 306–318
Candes EJ, Donoho DL (1999) Curvelets - a surprisingly effective nonadaptive representation for objects with edges,” In: Cohen A, Rabut C, Schumaker LL (eds) Curve and surface fitting: Saint-Malo
Chang C-C, Lin C-J: LIBSVM: a Library for Support Vector Machines, 2001, Available at http://www.csie.ntu.edu.tw/cjlin/libsvm
Cheng Q, Huang TS (2001) An additive approach to transform-domain information hiding and optimum detection structure. IEEE Trans Multimedia 3(3):273–284
Cho S, Cha B-H, Gawecki m, Jay Kuo C-C (2013) Block-based image steganalysis: algorithm and performance evaluation. J Vis Commun Image Represent 24(7):846–856
Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687
Dai M, Liu Y, Lin J (2008) Steganalysis based on feature reducts of rough set by using genetic algorithm, Proc. World Congress on Intelligent Control and Automation
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
Farid H (2002) Detecting hidden messages using higher-order statistical models. In: Image Processing. 2002. International Conference on, Rochester, NY, USA 905–908
Fridrich J (2000) Miroslav Goljan and Dorin Hogea, Attacking the OutGuess, Proc. ACM Intl. Conf. Information security
Fridrich J (2004) Feature-Based Steganalysis for JPEG Images and its Implications for Future Design of Steganographic Schemes, Proc. Int. Workshop. Information hiding, vol.3200, Lecture. Computer science
Fridrich J, Goljan M (2002) Practical steganalysis of digital images-state of the art. Proc SPIE 4675:1–13
Fridrich J, Goljan M, Hogea D (2003) New methodology for breaking steganographic techniques for JPEGs, Proc SPIE Electronic Imaging, Santa Clara, CA, pp. 143–155
Fu J-W, Qi Y-C, Yuan J-S (2007) Wavelet domain audio steganalysis based on statistical moments and PCA, Proc. IEEE Intl. Conf. Wavelet Analysis and Pattern recognition
Geetha S, Sivatha Sindhu SS, Kamaraj N (2008) Steganalysis of LSB Embedded Images based on Adaptive Threshold Close Color Pair Signature, in Sixth IEEE Indian Conference on Computer Vision, Graphics and Image Processing ICVGIP 2008
Goljan, Fridrich J (2015) CFA-aware features for steganalysis of color images, Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XVIISan Francisco, CA
Gonzalez FP, Balado F, Martin JRH (2003) Performance analysis of existing and new methods for data hiding with known-host information in additive channels. IEEE Trans Signal Process 51(4):960–980
Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Networks Learn Syst 27(7):1404–1416
Harmsen JJ (2003) Steganalysis of additive noise modelable information hiding. Master’s thesis, Rensselaer Polytechnic Institute, Troy, New York, USA
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press (reprinted in 1992 by MIT Press, Cambridge, MA)
Holotyak T, Fridrich J, Voloshynovskiy S (2005) Blind statistical steganalysis of additive steganography using wavelet higher order statistics. In: Lecture Notes in Computer Science, Springer Berlin, Heidelberg, pp. 273–274
Huang J, Shi YQ (1998) Adaptive image watermarking scheme based on visual masking. Electron Lett 34(8):748–750
Huo X (1999) Sparse image representation via combined transforms. PhD thesis, Stanford Univesity
Images. [Online]. Available: http://www.cl.cam.ac.uk/~fapp2/watermarking/benchmark/image_database.html
Katzenbeisser S, Petitcolas FAP (2000) Information hiding techniques for steganography and digital watermarking. Artech House, Norwood
Kaushal S, Anindya S, Manjunath BS (2007) YASS: Yet another steganographic scheme that resists blind steganalysis, 9th International Workshop on Information Hiding, Saint Malo, Brittany, France, Jun
Kim Y-S, Kwon O-H, Park R-H (1999) Wavelet based watermarking method for digital images using the human visual system. Electron Lett 35(6):466–468
Korejwa J. Jsteg shell 2.0. [Online]. Available: http://www.tiac.net/users/korejwa/steg.htm
Kutter M, Jordan F JK-PGS (Pretty Good Signature). [Online]. Available: http://ltswww.epfl.ch/~kutter/watermarking/JK_PGS.html
Lee YK, Chen LH (2000) High capacity image steganographic model. Proc Inst Elect Eng, Vis Image Signal Process 147(3):288–294
Li F, Zhang X, Chen B, Feng G (2013) JPEG steganalysis with high-dimensional features and Bayesian ensemble classifier. IEEE Signal Process Lett 20(3):233–236
Lie W-N, Chang L-C (1999) Data hiding in images with adaptive numbers of least significant bits based on human visual system, in Proc. IEEE Int. Conf. Image Processing, pp. 286–290
Lie W-N, Lin G-S (2005) A feature-based classification technique for blind image steganalysis. IEEE Trans Multimedia 7(6):1007–1020
Lie W-N, Lin G-S, Wu C-L, Wang T-C (2000) Robust image watermarking on the DCT domain. Proc IEEE Int Symp Circ System I:228–231
Lyu S, Farid H (2006) Steganalysis using Higher-Order Image statistics. Proc IEEE Trans Inf Forensic Secur, Vol.1, no.1
Manikopoulos C, Shi Y-Q, Song S, Zhang Z, Ni Z, Zou D (2002) Detection of block DCT-based steganography in gray-scale images. in Proc. 5th IEEE Workshop on Multimedia Signal Processing, pp. 355–358
Marvel LM, Boncelet CG Jr, Retter CT (1999) Spread spectrum image steganography. IEEE Trans Image Process 8(8):1075–1083
Min F (2007) A novel intrusion detection method based on combining ensemble learning with induction-Enhanced Particle Swarm Algorithm IEEE Third International Conference on Natural Computation (ICNC)
Nikolaidis N, Pitas I (1998) Robust image watermarking in the spatial domain. Signal Process 66:385–403
Ogihara T, Nakamura D, Yokoya N (1996) Data embedding into pictorial with less distortion using discrete cosine transform. In: Proc. ICPR’96, pp. 675–679
Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609
Petitcolas AP, Anderson RJ, Kuhn MG (1999) Information hiding—A survey. Proc IEEE 87(7):1062–1078
Pevny T, Fridrich J (2008) Multiclass detector of current steganographic methods for JPEG format. IEEE Trans Info Forensic Secur 3(4):635–650
PictureMarc, Embed Watermark, v 1.00.45, Digimarc Corp Available: http://avcibas.home.uludag.edu.tr/mmsp.pdf
Podilchuk CI, Wenjun Z (1998) Image-adaptive watermarking using visual models. IEEE J Select Areas Commun 16(4):525–539
Ru X-M, Zhang H-J, Huang X (2005) Steganalysis of audio: Attacking the steghide. Proc IEEE, Int Conf Mach Learn Cybern 7:3937–3942
Sajedi H, Jamzad M (2010) CBS: contourlet-based steganalysis method. J Signal Process Syst 61(1):367–373
Savoldi A, Gubian P (2007) Blind multi-class steganalysis system using wavelet statistics. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing IIHMSP'07, IEEE Computer Society, pp. 93–96
Shaohui L, Hongxun Y, Wen G (2003) Neural Network based Steganalysis in Still images. Proc Int’l Conf Multimedia Expo, ICME 2:509–512
Shi YQ, Xuan G, Yang C, Gao J, Zhang Z, Chai P, Zou D, Chen C, Chen W (2005) Effective steganalysis based on statistical moments of wavelet characteristic function. In: IEEE International Conference on Information Technology: Coding and Computing, ITCC&newapos;05, IEEE Computer Society, pp. 768–773
Starck J, Candes EJ, Donoho DL (2001) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684
Steganos II Security Suite.. [Online]. Available: http://www.steganos.com/english/steganos/download.htm
Xia Z, Wang X, Sun X, Liu Q, Xiong N (2014) Steganalysis of LSB matching using differences between nonadjacent pixels”, Multimedia Tools and Applications, Springer Verlag, pp. 1–16. doi: 10.1007/s11042-014-2381-8
Xia Z, Wang X, Sun X, Wang B (2014) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Networks 7(8):1283–1291
Xu B, Zhang Z, Wang J, Liu X (2007) Improved BSS based Schemes for Active steganalysis, Proc. ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and parallel distributed computing
Acknowledgments
This paper is based upon work supported by the All India Council for Technical Education - Research Promotion Scheme under Grant No. 20/AICTE/RIFD/RPS(POLICY-II)65/2012-13.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Muthuramalingam, S., Karthikeyan, N., Geetha, S. et al. Stego anomaly detection in images exploiting the curvelet higher order statistics using evolutionary support vector machine. Multimed Tools Appl 75, 13627–13661 (2016). https://doi.org/10.1007/s11042-015-2984-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2984-8