Abstract
Recent works have demonstrated that images with more texture regions should be selected as the sub-batch of covers to carry the total message when applying batch steganography to adaptive steganography and the core challenge of which is how to evaluate the texture complexity of image accurately according to the need of steganography security. In this paper, we first propose three methods for measuring the texture complexity of image to select images with highly textured content, then put forward our universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain. To assess the security of embedding strategies for batch adaptive steganography, we use a pooling steganalysis method based majority decision for the omniscient Warden, who informed by the average payload, embedding algorithm and cover source. Given a batch of images, our proposed embedding strategy is to select images with largest residual values to carry the total message, which is named max-residual-greedy (MRG) strategy. Experimental results show that the proposed embedding strategy outperforms the previous ones for batch adaptive steganography.
This work was supported in part by the National Natural Science Foundation of China under Grant U1636201 and 61572452.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2009)
Pevný, T., Fridrich, J.: Benchmarking for steganography. In: Solanki, K., Sullivan, K., Madhow, U. (eds.) IH 2008. LNCS, vol. 5284, pp. 251–267. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88961-8_18
Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3), 920–935 (2011)
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
Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 234–239. IEEE (2012)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Denemark, T., Sedighi, V., Holub, V, Cogranne, R., Fridrich, J.: Selection-channel-aware rich model for steganalysis of digital images. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 48–53. IEEE (2014)
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1–13 (2014)
Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4206–4210. IEEE (2014)
Guo, L., Ni, J., Su, W., Tang, C., Shi, Y.-Q.: Using statistical image model for JPEG steganography: uniform embedding revisited. IEEE Trans. Inf. Forensics Secur. 10(12), 2669–2680 (2015)
Wang, Z., Zhang, X., Yin, Z.: Hybrid distortion function for JPEG steganography. J. Electron. Imaging 25(5), 050501 (2016)
Wei, Q., Yin, Z., Wang, Z., Zhang, X.: Distortion function based on residual blocks for JPEG steganography. Multimed. Tools Appl. 77, 1–14 (2017)
Ker, A.D., Pevny, T.: Batch steganography in the real world. In: Proceedings of the on Multimedia and Security, MM&Sec 2012, pp. 1–10. ACM, New York (2012)
Guo, L., Ni, J., Shi, Y.Q.: An efficient JPEG steganographic scheme using uniform embedding. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 169–174. IEEE (2012)
Zhao, Z., Guan, Q., Zhao, X., Yu, H., Liu, C.: Embedding strategy for batch adaptive steganography. In: Shi, Y.Q., Kim, H.J., Perez-Gonzalez, F., Liu, F. (eds.) IWDW 2016. LNCS, vol. 10082, pp. 494–505. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53465-7_37
Zhao, Z., Guan, Q., Zhao, X., Yu, H., Liu, C.: Universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain. Multimed. Tools Appl. 77, 14093–14113 (2017)
Filler, T., Fridrich, J.: Gibbs construction in steganography. IEEE Trans. Inf. Forensics Secur. 5(4), 705–720 (2010)
Ker, A.D., Pevný, T., Kodovský, J., Fridrich, J.: The square root law of steganographic capacity. In: Proceedings of the 10th ACM Workshop on Multimedia and Security, MM&Sec 2008, pp. 107–116. ACM, New York (2008)
Wang, R., Ping, X., Niu, S., Zhang, T.: Segmentation based steganalysis of spatial images using local linear transform. In: Shi, Y.Q., Kim, H.J., Perez-Gonzalez, F., Liu, F. (eds.) IWDW 2016. LNCS, vol. 10082, pp. 533–549. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53465-7_40
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
Holub, V., Fridrich, J.: Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Trans. Inf. Forensics Secur. 10(2), 219–228 (2015)
Song, X., Liu, F., Yang, C., Luo, X., Zhang, Y.: Steganalysis of adaptive JPEG steganography using 2D gabor filters. In: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, pp. 15–23. ACM (2015)
Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, X., Chen, K., Zhang, W., Wang, Y., Yu, N. (2019). Improving the Embedding Strategy for Batch Adaptive Steganography. In: Yoo, C., Shi, YQ., Kim, H., Piva, A., Kim, G. (eds) Digital Forensics and Watermarking. IWDW 2018. Lecture Notes in Computer Science(), vol 11378. Springer, Cham. https://doi.org/10.1007/978-3-030-11389-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-11389-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11388-9
Online ISBN: 978-3-030-11389-6
eBook Packages: Computer ScienceComputer Science (R0)