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Deep neural networks for efficient steganographic payload location

  • Yu Sun
  • Hao Zhang
  • Tao Zhang
  • Ran Wang
Special Issue Paper
  • 8 Downloads

Abstract

The prevailing method for steganographic payload location aimed at LSB matching is the MAP method, which requires a few hundreds of stego images with load-carrying pixels at same locations and relatively high embedding rates. However, in practice, especially communication security, it is unwise for steganographers to generate stego images with high payloads or heavily utilize a same embedding key. Thus, the requirement of MAP is actually to some degree out of reach which leads to a performance degradation when faced with insufficient stego images with low embedding rates. To this end, we propose a tailored deep neural network (DNN) equipped with the improved feature named the “mean square of adjacency pixel difference”, which remarkably outperforms the previous state-of-the-art methods not only in terms of accuracy but also efficiency. Our approach can considerably reduce computational costs because no cover estimate, as represented by the key in MAP, is involved. This merit stems from the methodology we adopted that takes payload location as a binary classification problem for each pixel. Additionally, our DNN is consistently superior than MAP irrespective of embedding rates. The significance of our main design points in DNN and the improved features are verified, by experiment results. Besides, the time required in our method to handle 256 × 256 pixel images is 82.54 ms on the average, which is nearly 14 times faster than that of MAP. On the basis of relevant knowledge, the incorporation of feature extraction into DNN architecture is likely to enable future researchers to specify real-time payload locations.

Keywords

Steganalysis Payload location LSB matching DNN 

Notes

References

  1. 1.
    Provos, N., Honeyman, P.: Hide and seek: An introduction to steganography. IEEE Secur. Priv. 1, 32–44 (2003)CrossRefGoogle Scholar
  2. 2.
    Wang, H., Wang, S.: Cyber warfare: steganography vs. steganalysis. Commun. ACM 47, 76–82 (2004)CrossRefGoogle Scholar
  3. 3.
    Quach, T.T.: Optimal cover estimation methods and steganographic payload location. IEEE Trans. Inf. Forensics Secur. 6, 1214–1222 (2011)CrossRefGoogle Scholar
  4. 4.
    Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: Proc. IEEE Int. Workshop Inf. Forensics Secur. (WIFS), pp. 234–239 (2012)Google Scholar
  5. 5.
    Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1–13 (2014)CrossRefGoogle Scholar
  6. 6.
    Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Proc. Int. Workshop Inf. Hiding, pp. 161–177 (2010)Google Scholar
  7. 7.
    Qin, C., Chang, C., Chiu, Y.: A Novel Joint Data-Hiding and Compression Scheme Based on SMVQ and Image Inpainting. IEEE Trans. Image Process. 23(3), 969–978 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Chuan Qin, W.Z., Cao, F., Zhang, X., Chin-Chen, C.: Separable reversible data hiding in encrypted images via adaptive embedding strategy with block selection. Signal Process. 153, 109–122 (2018)CrossRefGoogle Scholar
  9. 9.
    Petitcolas, F.A.P., Anderson, R.J., Kuhn, M.G.: Information hiding-a survey, In: Proceedings of the IEEE, vol. 87, pp. 1062–1078 (1999)Google Scholar
  10. 10.
    Sharp, T.: (2001) An Implementation of Key-Based Digital Signal Steganography, International Workshop on Information Hiding, pp. 13–26 (2001)Google Scholar
  11. 11.
    Ker, A.D.: Locating steganographic payload via WS residuals. In: Proc. 10th Multimedia and Security Workshop, pp. 27–31. ACM (2008)Google Scholar
  12. 12.
    Ker, A.D., Lubenko, I.: Feature reduction and payload location with WAM steganalysis Proceedings of SPIE—The International Society for Optical Engineering, p. 7254 (2009)Google Scholar
  13. 13.
    Yan, X., Tao, Z., Ling, X., Ping, X.: New method for payload location aimed at LSB matching. J. Data Acquis. Process. 31(1), 145–151 (2016) (in Chinese) Google Scholar
  14. 14.
    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. Electron. Imaging 4, 1–11 (2016)CrossRefGoogle Scholar
  15. 15.
    Qian, Y., Dong, J., Wang, W., Tan, T.: (2015) Deep learning for steganalysis via convolutional neural networks, Proc. SPIE, vol. 9409, p. 94090J (2015)Google Scholar
  16. 16.
    Xu, G., Wu, H.Z., Shi, Y.Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23, 708–712 (2016)CrossRefGoogle Scholar
  17. 17.
    Chen, M., Fridrich, J., Boroumand, M.: (2018) Deep learning regressors for quantitative steganalysis. Electronic imaging, media watermarking, security, and forensics, pp. 160-1-160-7(7) (2018)Google Scholar
  18. 18.
    Xie, S., Girshick, R., Dollar, P., et al. Aggregated residual transformations for deep neural networks. In: IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society (2017)Google Scholar
  19. 19.
    Huang, G., Liu, Z., Laurens, V.D.M., Weinberger, K.Q.: Densely connected convolutional networks, pp. 2261–2269 (2016)Google Scholar
  20. 20.
    Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation International. J. Autom. Comput. 14, 119–135 (2017)CrossRefGoogle Scholar
  21. 21.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual learning for image recognition, pp 770–778 (2015)Google Scholar
  22. 22.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)Google Scholar
  23. 23.
    Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29, 3573–3587 (2015)Google Scholar
  24. 24.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009)CrossRefGoogle Scholar
  25. 25.
    Bas, P., Filler, T., Pevný, T.: Break our steganographic system: The Ins and outs of organizing BOSS. In Proc. Int. Workshop Inf. Hiding. Springer, Berlin, Heidelberg, pp. 59–70 (2011)Google Scholar
  26. 26.
    Bas, P., Furon, T.: Bows-2. http://bows2.gipsa-lab.inpg.fr (2007)
  27. 27.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding, 675–678 (2014)Google Scholar
  28. 28.
    Zeiler, M.D.: (2012) ADADELTA: An adaptive learning rate method computer scienceGoogle Scholar
  29. 29.
    Simonyan, K., Zisserman, A.: (2014) Very deep convolutional networks for large-scale image recognition computer scienceGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Digital Switching System Engineering and Technology Research CenterZhengzhouChina
  2. 2.School of Computer Science and EngineeringChangshu Institute of TechnologyChangshuChina

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