Skip to main content
SpringerLink
Log in

Steganalysis of convolutional neural network based on neural architecture search

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Recent studies show that the performance of deep convolutional neural network (CNN) applied to steganalysis is better than that of traditional methods. However, the existing network structure is still caused by artificial design, which may not be the optimal training network. This paper describes a deep residual network based on a neural architecture search (NAS) algorithm, to minimize the artificial design of network elements to achieve better detection results. In this algorithm, we add a long-span residual structure to the traditional layer of the residual structure, which can better capture the complex statistical information of digital images and actively enhance the signals from secret messages, which is suitable for distinguishing cover and stego images. Two of the most advanced steganographic algorithms, WOW (wavelet obtained weights) and SUNIWARD (spatial universal wavelet relative distortion), are used to evaluate the effectiveness of this model in the spatial domain. Compared with a recently proposed method based on CNN, our model achieves excellent performance on all tested algorithms for various payloads

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bas, P., Filler, T., Pevnỳ, T.: “break our steganographic system”: The ins and outs of organizing boss. In: International workshop on information hiding, pp. 59–70. Springer (2011)

  2. Boroumand, M., Chen, M., Fridrich, J.: Deep residual network for steganalysis of digital images. IEEE Transactions on Information Forensics and Security 14(5), 1181–1193 (2018)

    Article  Google Scholar 

  3. 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)

  4. Fan, L., Sun, W., Feng, G.: Image steganalysis via random subspace fisher linear discriminant vector functional link network and feature mapping. Mobile Networks & Applications (2019)

  5. Feng, G., Zhang, X., Ren, Y., Qian, Z., Li, S.: Diversity-based cascade filters for jpeg steganalysis. IEEE Transactions on Circuits and Systems for Video Technology 30(2), 376–386 (2020)

    Article  Google Scholar 

  6. Fridrich, J., Goljan, M., Hogea, D.: Steganalysis of jpeg images: Breaking the f5 algorithm. In: International Workshop on Information Hiding, pp. 310–323. Springer (2002)

  7. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security 7(3), 868–882 (2012)

    Article  Google Scholar 

  8. Fridrich, J., Pevnỳ, T., Kodovskỳ, J.: Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. In: Proceedings of the 9th workshop on Multimedia & security, pp. 3–14 (2007)

  9. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)

  10. Guo, L., Ni, J., Shi, Y.Q.: Uniform embedding for efficient jpeg steganography. IEEE transactions on Information Forensics and Security 9(5), 814–825 (2014)

    Article  Google Scholar 

  11. Guo, L., Ni, J., Su, W., Tang, C., Shi, Y.Q.: Using statistical image model for jpeg steganography: uniform embedding revisited. IEEE Transactions on Information Forensics and Security 10(12), 2669–2680 (2015)

    Article  Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

  13. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European conference on computer vision, pp. 630–645. Springer (2016)

  14. 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)

  15. Holub, V., Fridrich, J.: Random projections of residuals for digital image steganalysis. IEEE Transactions on information forensics and security 8(12), 1996–2006 (2013)

    Article  Google Scholar 

  16. Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security 2014(1), 1 (2014)

    Article  Google Scholar 

  17. Jin, Z., Feng, G., Ren, Y., Zhang, X.: Feature extraction optimization of jpeg steganalysis based on residual images. Signal Processing 170(5), 107455 (2020)

    Article  Google Scholar 

  18. Ker, A.D., Böhme, R.: Revisiting weighted stego-image steganalysis. In: Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, vol. 6819, p. 681905. International Society for Optics and Photonics (2008)

  19. Kodovskỳ, J., Fridrich, J.: Quantitative steganalysis of lsb embedding in jpeg domain. In: Proceedings of the 12th ACM workshop on Multimedia and security, pp. 187–198 (2010)

  20. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security 7(2), 432–444 (2011)

    Article  Google Scholar 

  21. 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)

  22. Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)

  23. Ni, D., Feng, G., Shen, L., Zhang, X.: Selective ensemble classification of image steganalysis via deep q network. IEEE Signal Processing Letters 26(7), 1065–1069 (2019)

    Article  Google Scholar 

  24. Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on information Forensics and Security 5(2), 215–224 (2010)

    Article  Google Scholar 

  25. Pevnỳ, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: International Workshop on Information Hiding, pp. 161–177. Springer (2010)

  26. Provos, N.: Defending against statistical steganalysis. Usenix security symposium 10, 323–336 (2001)

    Google Scholar 

  27. 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)

  28. Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. Proceedings of the aaai conference on artificial intelligence 33, 4780–4789 (2019)

    Article  Google Scholar 

  29. Sallee, P.: Model-based steganography. In: International workshop on digital watermarking, pp. 154–167. Springer (2003)

  30. Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Transactions on Information Forensics and Security 11(2), 221–234 (2015)

    Article  Google Scholar 

  31. Tan, S., Li, B.: Stacked convolutional auto-encoders for steganalysis of digital images. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, pp. 1–4. IEEE (2014)

  32. Steganographic algorithm jsteg: Software available at https://zooid. org/~paul/crypto/jsteg (1993)

  33. Westfeld, A.: F5-a steganographic algorithm. In: International workshop on information hiding, pp. 289–302. Springer, New York (2001)

  34. Xu, G., Wu, H.Z., Shi, Y.Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Processing Letters 23(5), 708–712 (2016)

    Article  Google Scholar 

  35. Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Transactions on Information Forensics and Security 12(11), 2545–2557 (2017)

    Article  Google Scholar 

  36. Zhong, K., Feng, G., Shen, L., Luo, J.: Deep learning for steganalysis based on filter diversity selection. Science China Information Sciences 61(12), 129105 (2018)

    Article  Google Scholar 

  37. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710 (2018)

Download references

Acknowledgements

This work was supported in part by National Basic Research Plan of China (JCKY2018415C001).

Author information

Authors and Affiliations

  1. Micro-Electronics Research Institute, Hangzhou Dianzi University, Zhejiang, China

    Hongbo Wang, Xingyu Pan, Lingyan Fan & Shuofeng Zhao

Authors
  1. Hongbo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingyu Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingyan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuofeng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingyan Fan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Pan, X., Fan, L. et al. Steganalysis of convolutional neural network based on neural architecture search. Multimedia Systems 27, 379–387 (2021). https://doi.org/10.1007/s00530-021-00779-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-021-00779-5

Keywords

Search

Navigation