Abstract
Steganography has been widely used to conceal secret information in multimedia content. Using generative adversarial networks (GAN) where two subnetworks compete against each other, steganography can learn good distortion measurement. Nevertheless, the convergence speed of GAN is usually slow, and the performance of GAN-based steganography has large room to improve. In this paper, we propose a new GAN-based spatial steganographic scheme. The proposed learning framework consists of two parts: a steganographic generator and a steganalytic discriminator. The former generates stego images, and the latter evaluates their steganography security. Different from existing GAN-based steganography, we reconstruct the generator by combining multiple feature maps, and then expand the maximum number of feature channels to 256. The reconstruction generator can effectively generate a sophisticated probability map, which is used to calculate optimal distortion measurement and further provides a better guidance for adaptive information embedding. Comprehensive experimental results show that, with the same discriminant network, the anti-steganalysis performance of our method is better than that of ASDL-GAN scheme and Yang’s scheme.
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
Notes
- 1.
Since RTX2080 GPU card has only 8 GB memory, 8 cover-stego pairs are the maximum number of images that the processor can process at one time.
References
Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)
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
Fridrich, J., Holub, V.: Designing steganographic distortion using directional filters. In: IEEE International Workshop on Information Forensics and Security, WIFS 2012, pp. 234–239. IEEE (2012). https://doi.org/10.1109/WIFS.2012.6412655
Li, B., Tan, S., Wang, M., Huang, J.: Investigation on cost assignment in spatial image steganography. IEEE Trans. Inf. Forensics Secur. 9(8), 1264–1277 (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). https://doi.org/10.1186/1687-417X-2014-1
Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inf. Forensics Secur. 11(2), 221–234 (2016)
Xu, G., Wu, H., Shi, Y.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Tang, W., Tan, S., Li, B., Huang, J.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 24(10), 1547–1551 (2017)
Yang, J., Liu, K., Kang, X., Wong, E.K.: Spatial image steganography based on generative adversarial network. arXiv:1804.07939 (2018)
Li, F., Wu, K., Zhang, X., Yu, J., Lei, J., Wen, M.: Robust batch steganography in social networks with non-uniform payload and data decomposition. IEEE Access 6, 29912–29925 (2018)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2016)
Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: secure steganography based on generative adversarial networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds.) PCM 2017. LNCS, vol. 10735, pp. 534–544. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77380-3_51
Li, F., Zhang, X., Cheng, H., Jiang, Y.: Digital image steganalysis based on local textural features and double dimensionality reduction. Secur. Commun. Netw. 9(8), 729–736 (2016)
Volkhonskiy, D., Nazarov, I., Borisenko, B., Burnaev, E.: Steganographic generative adversarial networks. arXiv:1703.05502 (2017)
Li, F., Wu, K., Lei, J., Wen, M., Bi, Z., Gu, C.: Steganalysis over large-scale social networks with high-order joint features and clustering ensembles. IEEE Trans. Inf. Forensics Secur. 11(2), 344–357 (2016)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 (2017)
Places365-Standard (2019). http://places2.csail.mit.edu/download.html. Accessed Mar 2019
DDE Download (2019). http://dde.binghamton.edu/download/. Accessed Mar 2019
Qian, Y., Dong, J., Wang W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Media Watermarking, Security, and Forensics 2015, Proceedings of SPIE, vol. 9409 (2015). https://doi.org/10.1117/12.2083479
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)
Zhang Y., Zhang W., Chen K., et al.: Adversarial examples against deep neural network based steganalysis. In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 67–72. ACM (2018)
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)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Acknowledgments
This work was supported by Natural Science Foundation of China under Grants (61602295, U1736120), the Foreign Visiting Scholar Program of Shanghai Municipal Education Commission and Postgraduate Innovation and Entrepreneurship Project of Shanghai University of Electric Power (A-0201-19-183Y-20).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, H., Li, F., Zhang, X., Wu, K. (2020). GAN-Based Steganography with the Concatenation of Multiple Feature Maps. In: Wang, H., Zhao, X., Shi, Y., Kim, H., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2019. Lecture Notes in Computer Science(), vol 12022. Springer, Cham. https://doi.org/10.1007/978-3-030-43575-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-43575-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43574-5
Online ISBN: 978-3-030-43575-2
eBook Packages: Computer ScienceComputer Science (R0)