Abstract
Despite the state-of-the-art steganalysis can detect highly undetectable steganography, it is too unreliable to implement in the real world due to its false alarm rate. In pooled steganalysis scenario, multiple objects are intercepted and a reliable collective decision is required. To control the reliability, the confidence intervals of the detectors’ false rates are estimated as a parameter and hypothesis testing technology is used to determine the threshold of stego rates. In view of the fact that the false rate is vulnerable to some image properties (e.g. image size, and texture complexity), we propose a novel fine-grained scheme where test sets are divided by its texture measure in both parameter estimation and hypothesis testing processes. The demonstration on public image sets shows the proposed scheme achieves higher reliability in most cases. It confirms that the priori knowledge of image properties is conductive to a accurate threshold and reliable decision.
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
Similar content being viewed by others
References
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). doi:10.1007/978-3-642-16435-4_13
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: IEEE Int’l Workshop on Information Forensics and Security (WIFS 2012), pp. 234–239. IEEE Press, New York (2012)
Holub, V., Fridrich, J.: Digital image steganography using universal distortion. In: ACM IH&MMSec 2013, pp. 59–68. ACM, New York (2013)
Pevný, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)
Denemark, T., Sedighi, V., Holub, V., Cogranne, R., Fridrich, J.: Selection-channel-aware rich model for steganalysis of digital images. In: IEEE WIFS 2014, pp. 48–53. IEEE Press, New York (2014)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2001)
Cogranne, R., Sedighi, V., Fridrich, J., Pevný, T.: Is ensemble classifier needed for steganalysis in high-dimensional feature spaces?. In: IEEE WIFS 2015, pp. 1–6. IEEE Press, New York (2015)
Ker, A.D.: Batch steganography and pooled steganalysis. In: Camenisch, J.L., Collberg, C.S., Johnson, N.F., Sallee, P. (eds.) IH 2006. LNCS, vol. 4437, pp. 265–281. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74124-4_18
Pevný, T., Ker, A.D.: Towards dependable steganalysis. In: Proceedings of Media Watermarking, Security, and Forensics 2015. SPIE, vol. 9409, pp. 94090I–94090I (2015)
Ker, A.D.: Perturbation hiding and the batch steganography problem. In: Solanki, K., Sullivan, K., Madhow, U. (eds.) IH 2008. LNCS, vol. 5284, pp. 45–59. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88961-8_4
Böhme, R.: Assessment of steganalytic methods using multiple regression models. In: Barni, M., Herrera-JoancomartĂ, J., Katzenbeisser, S., PĂ©rez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 278–295. Springer, Heidelberg (2005). doi:10.1007/11558859_21
Huang, W., Zhao, X.: Novel cover selection criterion for spatial steganography using linear pixel prediction error. Sci. Chin. Inf. Sci. 59(5), 05910301–05910303 (2016)
Ker, A.D.: Batch steganography and the threshold game. In: Proceedings of the International Society for Optical Engineering. SPIE, vol. 6505, pp. 650504–650504 (2007)
Pevný, T., Fridrich, J.: Multiclass detector of current steganographic methods for JPEG format. IEEE Trans. Inf. Forensics Secur. 3(4), 635–650 (2008)
Ker, A.D., Bas, P., Böhme, R., Cogranne, R., Craver, S., Filler, T., Fridrich, J., Pevný, T.: Moving steganography and steganalysis from the laboratory into the real world. In: First ACM Workshop on Information Hiding & Multimedia Security (IH&MMSec 2013), pp. 45–58 (2013)
Dabeer, O., Sullivan, K., Madhow, U., Chandrasekaran, S.: Detection of hiding in the least significant bit. IEEE Trans. Sig. Proc. 52(10), 3046–3058 (2004)
Qiao, T., Retraint, F., Cogranne, R., Zitzmann, C.: Steganalysis of JSteg algorithm using hypothesis testing theory. EURASIP J. Inf. Secur. 1, 1–16 (2015)
Cachin, C.: An information-theoretic model for steganography. In: Aucsmith, D. (ed.) IH 1998. LNCS, vol. 1525, pp. 306–318. Springer, Heidelberg (1998). doi:10.1007/3-540-49380-8_21
Cogranne, R., Fridrich, J.: Modeling and extending the ensemble classifier for steganalysis of digital images using hypothesis testing theory. IEEE Trans. Inf. Forensics Secur. 10(12), 2627–2642 (2015)
Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26(4), 404–413 (1934)
Brown, L.D., Cai, T.T., DasGupta, A.: Interval estimation for a binomial proportion. Stat. Sci. 16(2), 101–117 (2001)
Highleyman, W.H.: The design and analysis of pattern recognition experiments. Bell Syst. Tech. J. 41(2), 723–744 (1962)
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). doi:10.1007/978-3-642-24178-9_5
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61402390), the National Key Technology R&D Program (Grant No. 2014BAH41B01), and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA06030600).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Huang, W., Zhao, X. (2017). Reliable Pooled Steganalysis Using Fine-Grained Parameter Estimation and Hypothesis Testing. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-53465-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53464-0
Online ISBN: 978-3-319-53465-7
eBook Packages: Computer ScienceComputer Science (R0)