Abstract
Universal Steganalysis rely on extracting higher order statistical features that gets disturbed when hiding the message in a clean image. Due to content adaptive steganographies like HUGO, WOW etc. which embed the data more in textured areas of the image rather than smooth areas by minimizing the distortion of the image itself, first order features are not sufficient to differentiate clean and stego images. Thus, rich models come into picture in which a large number of features are extracted based on higher order noise residuals of clean and stego images. Thus, Universal Steganalyser is essentially a supervised classifier built on high dimensional feature set. To work with such high dimensional features on a large dataset of images is a very challenging task due to curse of dimensionality as well as computationally very expensive. This paper aims at comparing performance of three techniques-Ensemble classifier, Logistic regression and K-Nearest Neighbors on Spatial Rich Model features extracted for benchmarked dataset BOSSbase_1.01, for the better discrimination of clean and stego images.
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References
Provos, N., Honeyman, P.: Hide and seek: an introduction to steganography. IEEE Secur. Priv. 99(3), 32–44 (2003)
Zhang, T., Ping, X.: Reliable detection of LSB steganography based on the difference image histogram. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, (ICASSP 2003), vol. 3. IEEE (2003)
Westfeld, A., Pfitzmann, A.: Attacks on steganographic systems. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 61–76. Springer, Heidelberg (2000). https://doi.org/10.1007/10719724_5
Chan, C.-K., Cheng, L.-M.: Hiding data in images by simple LSB substitution. Pattern Recogn. 37(3), 469–474 (2004)
Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)
Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 161–177. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16435-4_13
Holub, V., Fridrich, J.J.: Designing steganographic distortion using directional filters. In: WIFS (2012)
Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Trans. Inf. Forensics Secur. 1(1), 111–119 (2006)
Wang, Y., Moulin, P.: Optimized feature extraction for learning-based image steganalysis. IEEE Trans. Inf. Forensics Secur. 2(1), 31–45 (2007)
Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)
Liu, Q., Sung, A.H., Qiao, M.: Neighboring joint density-based JPEG steganalysis. ACM Trans. Intell. Syst. Technol. (TIST) 2(2), 16 (2011)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Holub, V., Fridrich, J.: Random projections of residuals for digital image steganalysis. IEEE Trans. Inf. Forensics Secur. 8(12), 1996–2006 (2013)
Bas, P., Filler, T., Pevný, T.: “Break our steganographic system”: the ins and outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24178-9_5
http://dde.binghamton.edu/download/. Accessed 10 July 2019
Kodovský, J., Fridrich, J.J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)
Lubenko, I., Ker, A.D.: Steganalysis using logistic regression. In: Media Watermarking, Security, and Forensics III, vol. 7880, pp. 78800K. International Society for Optics and Photonics (2011)
Mohammadi, F.G., Abadeh, M.S.: A new metaheuristic feature subset selection approach for image steganalysis. J. Intell. Fuzzy Syst. 27(3), 1445–1455 (2014)
Guettari, N., Capelle-Laizé, A.S., Carré, P.: Blind image steganalysis based on evidential k-nearest neighbors. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2742–2746. IEEE (2016)
Cawley, G.C., Talbot, N.L.: Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 22(19), 2348–2355 (2006)
https://www.csie.ntu.edu.tw/~cjlin/liblinear/. Accessed 02 Sept 2019
Lu, J., Liu, F., Luo, X.: Selection of image features for steganalysis based on the Fisher criterion. Digit. Invest. 11(1), 57–66 (2014)
Mohammadi, F.G., Abadeh, M.S.: Image steganalysis using a bee colony based feature selection algorithm. Eng. Appl. Artif. Intell. 31, 35–43 (2014)
Chhikara, R.R., Sharma, P., Singh, L.: A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis. Int. J. Mach. Learn. Cybern. 7(6), 1195–1206 (2015). https://doi.org/10.1007/s13042-015-0448-0
Ma, Y., et al.: Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Trans. Circuits Syst. Video Technol. 29, 336–350 (2018)
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Gupta, A., Chhikara, R., Sharma, P. (2020). Comparing Classifiers for Universal Steganalysis. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_14
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DOI: https://doi.org/10.1007/978-981-15-5827-6_14
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