Skip to main content
SpringerLink
Log in

A Deep Residual Multi-scale Convolutional Network for Spatial Steganalysis

  • Conference paper
  • First Online:
Digital Forensics and Watermarking (IWDW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11378))

Included in the following conference series:

Abstract

Recent studies have indicated that Convolutional Neural Network (CNN), incorporated with certain domain knowledge, is capable of achieving competitive performances on discriminating trivial perturbation introduced by spatial steganographic schemes. In this paper, we propose a deep residual multi-scale convolutional network model, which outperforms several CNN-based steganalysis schemes and hand-crafted rich models. Compared to CNN-based steganalyzers proposed in recent studies, our model has a deeper network structure and it is integrated with a series of proven elements and complicated convolutional modules. With the intention of abstracting features from various dimensions, multi-scale convolutional modules are designed in three different ways. Besides, inspired by the idea of residual learning, shortcut components are adopted in the proposed model. Extensive experiments with BOSSbase v1.01 and LIRMMBase are carried out, which demonstrates that our network is able to detect multiple state-of-the-art spatial embedding schemes with different payloads.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bas, Patrick, Filler, Tomáš, Pevný, Tomáš: “Break our steganographic system”: the ins and outs of organizing BOSS. In: Filler, Tomáš, Pevný, Tomáš, Craver, Scott, Ker, Andrew (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24178-9_5

    Chapter  Google Scholar 

  2. Bianchini, M., Scarselli, F.: On the complexity of neural network classifiers: a comparison between shallow and deep architectures. IEEE Trans. Neural Netw. Learn. Syst. 25(8), 1553–1565 (2014)

    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: Proceedings of the 2014 IEEE International Workshop on Information Forensics and Security, Atlanta, GA, USA, pp. 48–53 (2014)

    Google Scholar 

  4. Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3–2), 920–935 (2011)

    Article  Google Scholar 

  5. Fridrich, J., Kodovský, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)

    Article  Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770–778 (2016)

    Google Scholar 

  7. Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security, Costa Adeje, Tenerife, Spain, pp. 234–239 (2012)

    Google Scholar 

  8. Holub, V., Fridrich, J.: Random projections of residuals for digital image steganalysis. IEEE Trans. Inf. Forensics Secur. 8(12), 1996–2006 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 2261–2269 (2017)

    Google Scholar 

  11. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, Lille, France, pp. 448–456 (2015)

    Google Scholar 

  12. Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Proceedings of the 12th International Workshop on Information Hiding, Calgary, AB, Canada, pp. 161–177 (2010)

    Google Scholar 

  13. Pibre, L., Pasquet, J., Ienco, D., Chaumont, M.: Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch. In: Proceedings of the 2016 IS&T, Electronic Imaging, Media Watermarking, Security, and Forensics, San Francisco, CA, USA, pp. 1–11 (2016)

    Google Scholar 

  14. Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Proceedings of the 2015 IS&T, Electronic Imaging, Media Watermarking, Security, and Forensics, San Francisco, CA, USA, p. 94090J (2015)

    Google Scholar 

  15. Qian, Y., Dong, J., Wang, W., Tan, T.: Learning and transferring representations for image steganalysis using convolutional neural network. In: Proceedings of the 2016 IEEE International Conference on Image Processing, Phoenix, AZ, USA, pp. 2752–2756 (2016)

    Google Scholar 

  16. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556

  17. Sun, S., Chen, W., Wang, L., Liu, X., Liu, T.: On the depth of deep neural networks: a theoretical view. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, pp. 2066–2072 (2016)

    Google Scholar 

  18. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, pp. 4278–4284 (2017)

    Google Scholar 

  19. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 1–9 (2015)

    Google Scholar 

  20. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 2818–2826 (2016)

    Google Scholar 

  21. Tan, S., Li, B.: Stacked convolutional auto-encoders for steganalysis of digital images. In: Proceedings of the 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Chiang Mai, Thailand, pp. 1–4 (2014)

    Google Scholar 

  22. Wu, S., Zhong, S., Liu, Y.: Steganalysis via deep residual network. In: Proceedings of the 22nd IEEE International Conference on Parallel and Distributed Systems, Wuhan, China, pp. 1233–1236 (2016)

    Google Scholar 

  23. Wu, S., Zhong, S., Liu, Y.: Residual convolution network based steganalysis with adaptive content suppression. In: Proceedings of the 2017 IEEE International Conference on Multimedia and Expo, Hong Kong, China, pp. 241–246 (2017)

    Google Scholar 

  24. Wu, S., Zhong, S., Liu, Y.: Deep residual learning for image steganalysis. Multimedia Tools Appl. 77(9), 10437–10453 (2018)

    Article  Google Scholar 

  25. Xu, G.: Deep convolutional neural network to detect J-UNIWARD. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, Philadelphia, PA, USA, pp. 67–73 (2017)

    Google Scholar 

  26. Xu, G., Wu, H., Shi, Y.Q.: Ensemble of CNNs for steganalysis: an empirical study. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, Vigo, Galicia, Spain, pp. 103–107 (2016)

    Google Scholar 

  27. Xu, G., Wu, H., Shi, Y.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)

    Article  Google Scholar 

  28. Yang, J., Liu, K., Kang, X., Wong, E.K., Shi, Y.: Steganalysis based on awareness of selection-channel and deep learning. In: Proceedings of the 16th International Workshop on Digital Forensics and Watermarking, Magdeburg, Germany, pp. 263–272 (2017)

    Google Scholar 

  29. Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)

    Article  Google Scholar 

  30. Yedroudj, M., Chaumont, M., Comby, F.: How to augment a small learning set for improving the performances of a CNN-based steganalyzer? CoRR abs/1801.04076 (2018). http://arxiv.org/abs/1801.04076

  31. Yedroudj, M., Comby, F., Chaumont, M.: Yedrouj-net: an efficient CNN for spatial steganalysis. CoRR abs/1803.00407 (2018). http://arxiv.org/abs/1803.00407

  32. Yuan, Y., Lu, W., Feng, B., Weng, J.: Steganalysis with CNN using multi-channels filtered residuals. In: Proceedings of the 3rd International Conference on Cloud Computing and Security, Nanjing, China, pp. 110–120 (2017)

    Google Scholar 

  33. Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012). http://arxiv.org/abs/1212.5701

  34. Zhu, X., Liu, J., Wang, J., Li, C., Lu, H.: Sparse representation for robust abnormality detection in crowded scenes. Pattern Recogn. 47(5), 1791–1799 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by NSFC under 61802393, U1636102, U1736214 and 61872356, National Key Technology R&D Program under 2016YFB0801003 and 2016QY15Z2500, and Project of Beijing Municipal Science & Technology Commission under Z181100002718001.

Author information

Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Shiyang Zhang, Hong Zhang, Xianfeng Zhao & Haibo Yu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100093, China

    Shiyang Zhang, Hong Zhang, Xianfeng Zhao & Haibo Yu

Authors
  1. Shiyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianfeng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haibo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Zhang .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Chang D. Yoo

  2. New Jersey Institute of Technology, Newark, NJ, USA

    Yun-Qing Shi

  3. Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

  4. University of Florence, Florence, Italy

    Alessandro Piva

  5. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Gwangsu Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, S., Zhang, H., Zhao, X., Yu, H. (2019). A Deep Residual Multi-scale Convolutional Network for Spatial Steganalysis. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11389-6_4

  • 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)

Publish with us

Policies and ethics

Search

Navigation