Abstract
Blind quantitative steganalysis is about revealing more details about hidden information without any prior knowledge of steganograghy. Machine learning can be used to estimate some properties of hidden message for blind quantitative steganalysis. We propose a quantitative steganalysis method based on fusion of different steganalysis features and the estimator relies on gradient boosting. Experimental result shows that our proposed method has good performance for quantitative steganalysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fridrich, J., Miroslav, G., Dorin, H.: Steganalysis of JPEG images: Breaking the F5 algorithm. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 310–323. Springer, Heidelberg (2003)
Li, B., Shi, Y.Q., Huang, J.: Steganalysis of YASS. IEEE Transactions On Information Forensics And Security 4, 369–382 (2009)
Fridrich, J., Goljan, M., Hogea, D., Soukal, D.: Quantitative steganalysis:Estimating secret message length. ACM Multimedia Systems Journal. Special issue on multimedia Security 9(3), 288–302 (2003)
Pevný, T., Fridrich, J., Ker, A.D.: From blind to quantitative steganalysis. In: Media Forensics and Security, vol. 7254 (2009)
Pevný, T., Fridrich, J.: Multiclass Detector of Current Steganographic Methods for JPEG Format. IEEE Transactions On Information Forensics And Security 3(4), 635–650 (2008)
Shi, Y.Q., Chen, C., Chen, W.: A markov process based approach to effective attacking JPEG steganography. In: Camenisch, J.L., Collberg, C.S., Johnson, N.F., Sallee, P. (eds.) IH 2006. LNCS, vol. 4437, pp. 249–264. Springer, Heidelberg (2007)
Friedman, J.H.: Greedy Function Approximation: A Gradient Boos-Ting Machine. The Annals of Statistics 29(5), 1189–1232 (2001)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regr-Ession: A Statistical View Of Boosting. The Annals of Statistics 28(2), 337–407 (2000)
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)
Liu, W.-M., Chang, C.-I.: Variants of Principal Compo-nents Analysis. In: IEEE International Geoscience and Remote Sensing Symposium (July 2007)
Bartlett, M.: Further aspects of the theory of multiple regression. Proceedings of the Cambridge Philosophical Society (34) (1938)
Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1222–1228 (2004)
Uncompressed Color Image Database, http://www.staff.lboro.ac.uk/~cogs/datasets/UCID/u-cid.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guan, Q., Dong, J., Tan, T. (2011). Blind Quantitative Steganalysis Based on Feature Fusion and Gradient Boosting. In: Kim, HJ., Shi, Y.Q., Barni, M. (eds) Digital Watermarking. IWDW 2010. Lecture Notes in Computer Science, vol 6526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18405-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-18405-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18404-8
Online ISBN: 978-3-642-18405-5
eBook Packages: Computer ScienceComputer Science (R0)