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An improved steganography without embedding based on attention GAN

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Abstract

Steganography is an art to hide information in the carriers to prevent from being detected, while steganalysis is the opposite art to detect the presence of the hidden information. With the development of deep learning, several state-of-the-art steganography and steganalysis based on deep learning techniques have been proposed to improve hiding or detection capabilities. Generative Adversarial Networks (GANs) based steganography directly uses the minimax game between the generator and discriminator, to automatically generate steganography algorithms resisting being detected by powerful steganalysis. The steganography without embedding (SwE) based on GANs, where the generated cover images themselves are stego images carrying secret information has shown its state-of-the-art steganography performance. However, SwE based on GANs has serious weaknesses, such as low information recovery accuracy, low steganography capacity and poor natural showing. To solve these problems, this paper proposes a new SwE based on attention-GAN model, with carefully designed generator, discriminator and extractor, as well as their loss functions and optimized training mode. The generative model utilizes the attention method to improve the correlation among pixels and to correct errors such as image distortion and background abnormality. The soft margin discriminator is used to improve the compatibility of information recovery and fault tolerance of image generation. Experimental evaluations show that our method can achieve a very high information recovery accuracy (100% in some cases), and at the same time improve the steganography capacity and image quality.

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Notes

  1. FFHQ is available at https://github.com/NVlabs/ffhq-dataset.

  2. http://ai.stanford.edu/~jkrause/cars

References

  1. Barni M (2011) Steganography in digital media: Principles, algorithms, and applications [book reviews]. IEEE Signal Process Mag 28:142–144

    Article  Google Scholar 

  2. Zhou ZL, Cao Y, Sun XM (2016) Coverless information hiding based on bag-of-words model of image. J Appl Sci 34:527–536

    Google Scholar 

  3. Zheng S, Wang L, Ling B, Hu D (2017) .. In: Coverless information hiding based on robust image hashing. Springer International Publishing, Cham, pp 536–547

  4. Xu J, Mao X, Jaffer A, Jaffer A, Lu S, Li L, Toyoura M (2015) Hidden message in a deformation-based texture. Vis Comput Int J Computer Graphics 31:1653–1669

    Google Scholar 

  5. Hu D, Liang W, Wenjie J, Shuli Z, Bin L (2018) A novel image steganography method via deep convolutional generative adversarial networks. IEEE Access PP:1–1

    Google Scholar 

  6. Li S, Zhang X (2019) Towards construction based data hiding: From secrets to fingerprint images. IEEE Trans Image Process 28:1–1

    Article  MathSciNet  Google Scholar 

  7. Zhang Z, Liu J, Ke Y, Lei Y, Li J, Zhang M, Yang X (2019) Generative steganography by sampling. IEEE Access 7:118586–118597

    Article  Google Scholar 

  8. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp 2672–2680

  9. Radford A, Metz L (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. cite arxiv:1511.06434Comment: Under review as a conference paper at ICLR

  10. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Precup D, Teh YW (eds) Proceedings of the 34th international conference on machine learning. Volume 70 of Proceedings of Machine Learning Research., International Convention Centre, Sydney, Australia, PMLR , pp 214–223

  11. Jiang W, Hu D, Yu C, Li M, Zhao Z (2020) A new steganography without embedding based on adversarial training. In: ACM Turing Celebration Conference - China (ACM TURC’20), p 5

  12. Mielikainen J (2006) LSB matching revisited. IEEE Signal Process Lett 13:285–287

    Article  Google Scholar 

  13. Pevný T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: Böhme R, Fong PWL, Safavi-Naini R (eds) Information hiding. Springer, Berlin, pp 161–177

  14. Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 2014:1

    Article  Google Scholar 

  15. Holub V, Fridrich J (2013) Designing steganographic distortion using directional filters. In: IEEE International workshop on information forensics and security, pp 234–239

  16. Guo L, Ni J, Shi Y (2012) An efficient jpeg steganographic scheme using uniform embedding. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp 169–174

  17. Guo L, Ni J, Su W, Tang C, Shi Y (2015) Using statistical image model for jpeg steganography: Uniform embedding revisited. IEEE Trans Inf Forensics Secur 10:2669–2680

    Article  Google Scholar 

  18. Volkhonskiy D, Nazarov I, Borisenko B, Burnaev E (2017) Steganographic generative adversarial networks. Proceedings of NIPS 2016 Workshop on adversarial training. arXiv:1703.05502

  19. Shi H, Dong J, Wang W, Qian Y, Zhang X (2018) SSGAN: Secure steganography based on generative adversarial networks. In: Zeng B, Huang Q, El Saddik A, Li H, Jiang S, Fan X (eds) Advances in Multimedia Information Processing – PCM 2017. Springer International Publishing, Cham, pp 534–544

  20. Tang W, Tan S, Li B, Huang J (2017) Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process Lett PP:1–1

    Google Scholar 

  21. Yang J, Liu K, Kang X, Wong EK, Shi Y (2018) Spatial image steganography based on generative adversarial network. arXiv:1804.07939

  22. Hayes J, Danezis G (2017) Generating steganographic images via adversarial training. arXiv:1703.00371

  23. Zhu J, Kaplan R, Johnson J, Fei-Fei L (2018) Hidden: Hiding data with deep networks. arXiv:1807.09937

  24. Baluja S (2017) Hiding images in plain sight: Deep steganography. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30. Curran Associates, Inc., pp 2069–2079

  25. Wu P, Yang Y, Xiaoqiang L (2018) Stegnet: Mega image steganography capacity with deep convolutional network. Future Internet 10:54–

  26. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser U, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, NY, USA, pp 6000–6010. Curran Associates Inc.

  27. Zhao Z, Zheng P, Xu St, Wu X (2019) Object detection with deep learning: A review. IEEe Trans Neural Netw Learn Sys 30:3212–3232

    Article  Google Scholar 

  28. Moore R, DeNero J (2011) L1 and l2 regularization for multiclass hinge loss models. In: Symposium on machine learning in speech and language processing

  29. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems. vol 30, pp 6626–6637. Curran Associates, Inc.

  30. Ni J, Ye J, YI Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12:2545–2557

    Article  Google Scholar 

  31. Karras T, Laine S, Aila T (2019 ) A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 4396–4405

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) under grants Nos. U1836102, 61672203, &61976079, 62002094. Anhui Provincial Natural Science Foundation under the grant No. 2008085MF196.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, China

    Cong Yu, Donghui Hu, Shuli Zheng, Wenjie Jiang, Meng Li & Zhong-qiu Zhao

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  1. Cong Yu
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  2. Donghui Hu
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  3. Shuli Zheng
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  4. Wenjie Jiang
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  5. Meng Li
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  6. Zhong-qiu Zhao
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Correspondence to Donghui Hu.

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This article is part of the Topical Collection: Special Issue on Privacy-Preserving Computing

Guest Editors: Kaiping Xue, Zhe Liu, Haojin Zhu, Miao Pan and David S.L. Wei

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Yu, C., Hu, D., Zheng, S. et al. An improved steganography without embedding based on attention GAN. Peer-to-Peer Netw. Appl. 14, 1446–1457 (2021). https://doi.org/10.1007/s12083-020-01033-x

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