Abstract
Steganography is designed to conceal a secret message within public media. Traditional steganography needs a lot of expert knowledge and complex artificial rules. To solve this problem, we propose a novel self-learning steganographic algorithm based on the generative adversarial network, which we called SSteGAN. This method learns the steganographic algorithm in an unsupervised manner without expert knowledge and directly generates the stego image from the secret message without the cover image. We define a game with four parts: Alice, Bob, Dev and Eve. Alice and Bob attempt to communicate securely. Eve eavesdrops on their conversation and wants to distinguish whether the secret message is embedded in the image. Dev attempts to determine real images from generated images. Experiment results demonstrate that Alice can produce vivid stego images and Bob can successfully decode the secret message with \( 98.8\% \) accuracy.
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References
Abadi, M., Andersen, D.G.: Learning to protect communications with adversarial neural cryptography. arXiv preprint arXiv:1610.06918 (2016)
Baluja, S.: Hiding images in plain sight: deep steganography. In: Advances in Neural Information Processing Systems, pp. 2066–2076 (2017)
Berthelot, D., Schumm, T., Metz, L.: BEGAN: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017)
Goodfellow, I.J., et al.: Generative adversarial networks. Adv. Neural Inf. Process. Syst. 3, 2672–2680 (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5769–5779 (2017)
Hayes, J., Danezis, G.: Generating steganographic images via adversarial training. In: Advances in Neural Information Processing Systems, pp. 1951–1960 (2017)
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)
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1 (2014)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
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
Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Media Watermarking, Security, and Forensics 2015, vol. 9409, p. 94090J. International Society for Optics and Photonics (2015)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
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
Volkhonskiy, D., Nazarov, I., Borisenko, B., Burnaev, E.: Steganographic generative adversarial networks. arXiv preprint arXiv:1703.05502 (2017)
Acknowledgments
This research was supported by the National Key Research and Development Program of China (No. 2016YFB0800504), the National Natural Science Foundation of China (No. U163620068) and the Strategy Cooperation Project (AQ-18-01).
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Wang, Z., Gao, N., Wang, X., Qu, X., Li, L. (2018). SSteGAN: Self-learning Steganography Based on Generative Adversarial Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_22
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DOI: https://doi.org/10.1007/978-3-030-04179-3_22
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