Abstract
Targeted steganalysis aims at detecting hidden data embedded by a particular algorithm without any knowledge of the ‘cover’ image. In this paper we propose a novel approach for detecting Perturbed Quantization Steganography (PQ) by HFS (Huffman FR index Steganalysis) algorithm using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index) which is not yet explored by steganalysts. JPEG images spanning a wide range of sizes, resolutions, textures and quality are used to test the performance of the model. In this work we evaluate the model against several classifiers like Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Random Forests (RF) and Support Vector Machines (SVM) for steganalysis. Experiments conducted prove that the proposed HFS algorithm can detect PQ of several embedding rates with a better accuracy compared to the existing attacks.
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References
Birgit, P.: Information hiding terminology-results of an informal plenary meeting and additional proposals. In: The Proceedings of the First International Workshop on Information Hiding, pp. 347–350 (1996)
Miche, Y., Bas, P., Lendasse, A., Jutten, C., Simula, O.: Reliable steganalysis using a minimum set of samples and features. EURASIP Journal of Information Security, 350–354 (2009)
Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography. ACM Multimedia and Security Journal 11(2), 98–107 (2005)
Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography using wet paper codes. In: The Proceedings of ACM MM&S Workshop, Germany, pp. 4–15 (2004)
Fridrich, J., Pevny, T., Kodovsky, J.: Statistically undetectable JPEG steganography: dead ends. Challenges, and Opportunities. In: The Proceedings of ACM MM&S Workshop, Dallas, TX, pp. 3–14 (2007)
Hedieh, S., Mansour, J.: CBS: contourlet-based steganalysis method. Journal of Signal Processing Systems (2010)
Kharrazi, M., Sencar, H.T., Memon, N.: Benchmarking steganographic and steganalysis techniques. Journal of Electronic Imaging 15(4) (2006)
Zuzana, O., Jiri, H., Ivan, Z., Roman, S.: Steganography detection by means of neural network. In: The Ninteenth International Conference on Database and Expert Systems Application, pp. 571–574. IEEE Computer Society, Los Alamitos (2008)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Chiew, K.L., Pieprzyk, J.: Blind steganalysis: a countermeasure for binary image steganography. In: The Proceedings of the International Conference on Availability, Reliability and Security, pp. 653–658 (2010)
Dautrich, J.: Multi-class steganalysis. Machine Learning Course Research Project Distinguishing Images Embedded using Reversible Steganographic Schemes (2009)
Yoan, M., Benoit, R., Amaury, L., Patrick, B.: A feature selection methodology for steganalysis. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 49–56. Springer, Heidelberg (2006)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: The IEEE Eleventh International Conference on Computer Vision, pp. 1–8 (2007)
Bhat, V.H., Krishna, S., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.: HUBFIRE - A multi-class SVM based JPEG steganalysis using HBCL statistics and FR index. In: SECRYPT (in press, 2010)
Kharrazi., M., Sencar., H.T., Memon, N.: A performance study of common image steganography and steganalysis techniques. Journal of Electronic Imaging 15(4) (2006)
Fawcett, T.: ROC graphs: notes and practical considerations for data mining researchers. Technical Report HPL-2003–4. HP Laboratories, Palo Alto (2003)
Bishop, C.: Neural networks for pattern recognition. Oxford, Oxford University, UK (1995)
Ho, T.: Random decision forest. In: The Third International Conference on Document Analysis and Recognition, pp. 278–282 (1995)
http://debian.mc.vanderbilt.edu/R/CRAN/web/packages/randomForest/randomForest.pdf
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Gül, G., Dirik, A.E., Avcıbas, I.: Steganalytic features for JPEG compression-based perturbed quantization. IEEE Signal Processing Letters 14(3), 205–208 (2007)
Fridrich, J.: Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 67–81. Springer, Heidelberg (2004)
Avcibas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP Journal on Applied Signal Processing, 2749–2757 (2005)
Lyu., S., Farid, H.: Steganalysis using color wavelet statistics and one-class support vector machines. In: SPIE Symposium on Electronic Imagining, San Jose, CA (2004)
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Bhat, V.H., Krishna, S., Deepa Shenoy, P., Venugopal, K.R., Patnaik, L.M. (2011). Steganalysis of Perturbed Quantization Using HBCL Statistics and FR Index. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_6
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