Abstract
Conventional steganographic schemes are based on optimizing an additive distortion function, which is defined by summing up the cost of modified pixels. Using such schemes, pixels with lower costs will be selected for modification. However, the interactions of embedding changes are not explicitly considered in the distortion function. In this paper, we propose a new framework for steganography that incorporates a term considering interactions of embedding changes in the distortion function. An algorithm is designed to minimize the distortion with an approximal but efficient solution using auxiliary costs. The proposed framework can eliminate the ambiguity in the definition of additive distortion caused by updated costs. Experimental results show the proposed framework is more resilient to steganalysis compared with the schemes with a conventional additive distortion function, and it works as best as the embedding schemes with synchronized modifications.
This work is supported in part by NSFC (Grant 61572329, 61772349, and U1636202) and in part by the Shenzhen R&D Program (Grant JCYJ20160328144421330).
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
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
Denemark, T., Fridrich, J.: Improving steganographic security by synchronizing the selection channel. In: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, pp. 5–14 (2015)
Denemark, T., Fridrich, J., Holub, V.: Further study on the security of S-UNIWARD. In: SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics, pp. 902805-1–902805-13 (2014)
Denemark, T., Sedighi, V., Holub, V., Cogranne, R., Fridrich, J.: Selection-channel-aware rich model for steganalysis of digital images. In: Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 48–53. IEEE (2014)
Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3–2), 920–935 (2011)
Fridrich, J., Kodovský, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Fridrich, J.: Minimizing the embedding impact in steganography. In: The Workshop on Multimedia and Security, pp. 2–10 (2006)
Fridrich, J.: Digital image steganography using universal distortion. In: ACM Workshop on Information Hiding and Multimedia Security, pp. 59–68 (2013)
Fridrich, J.J., Kodovskỳ, J.: Multivariate Gaussian model for designing additive distortion for steganography. In: Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2949–2953 (2013)
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014, 1–13 (2014)
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: IEEE International Workshop on Information Forensics and Security, pp. 234–239 (2012)
Ker, A.D., et al.: Moving steganography and steganalysis from the laboratory into the real world. In: Proceedings of the first ACM Workshop on Information Hiding and Multimedia Security, pp. 45–58. ACM (2013)
Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)
Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: Proceeding of IEEE International Conference on Image Processing, pp. 4026–4210 (2014)
Li, B., Wang, M., Li, X., Tan, S., Huang, J.: A strategy of clustering modification directions in spatial image steganography. IEEE Trans. Inf. Forensics Secur. 10(9), 1905–1917 (2015)
Li, B., He, J., Huang, J., Shi, Y.Q.: A survey on image steganography and steganalysis. Dep. Comput. 2(3), 288–289 (2011)
Li, B., Li, Z., Zhou, S., Tan, S., Zhang, X.: New steganalytic features for spatial image steganography based on derivative filters and threshold LBP operator. IEEE Trans. Inf. Forensics Secur. 13(5), 1242–1257 (2018)
Pan, F., Li, J., Li, X., Guo, Y.: Steganography based on minimizing embedding impact function and HVS. In: International Conference on Electronics, Communications and Control, pp. 490–493 (2011)
Pevny, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Proceedings of International Workshop on Information Hiding, pp. 161–177 (2010)
Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inf. Forensics Secur. 11(2), 221–234 (2016)
Tan, S., Zhang, H., Li, B., Huang, J.: Pixel-decimation-assisted steganalysis of synchronize-embedding-changes steganography. IEEE Trans. Inf. Forensics Secur. 12(7), 1658–1670 (2017)
Tang, W., Li, H., Luo, W., Huang, J.: Adaptive steganalysis based on embedding probabilities of pixels. IEEE Trans. Inf. Forensics Secur. 11(4), 734–745 (2016)
Xu, G., Wu, H.Z., Shi, Y.Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)
Zeng, J., Tan, S., Li, B., Huang, J.: Large-scale JPEG steganalysis using hybrid deep-learning framework. IEEE Trans. Inf. Forensics Secur. 13(5), 1200–1214 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, H., Li, B., Tan, S. (2018). A New Steganographic Distortion Function with Explicit Considerations of Modification Interactions. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-00009-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00008-0
Online ISBN: 978-3-030-00009-7
eBook Packages: Computer ScienceComputer Science (R0)