Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Dataset mismatched steganalysis using subdomain adaptation with guiding feature

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The generalization problem in deep learning has always been an important problem to be solved. In the field of steganalysis, generalization is also an important factor that makes steganalysis models difficult to deploy in real-world scenarios. For a group of suspicious images that never appeared in the training set, the pre-trained deep learning-based steganalysis models tend to suffer from distinct performance degradation. To address this limitation, in this paper, a feature-guided subdomain adaptation steganalysis framework is proposed to improve the performance of the pre-trained models when detecting new data. Initially, the source domain and target domain will be divided into subdomains according to class, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterward, the guiding feature is generated to make the division of subdomains more stable and precise. When it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads, the experimental results show that the proposed steganalysis framework can significantly improve the average accuracy of SRNet model by 5.4% at 0.4bpp, 8.5% at 0.2bpp, and 8.0% at 0.1bpp in the case of dataset mismatch.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Holub, V., Fridrich, J., & Denemark, T. (2014). Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security, 2014(1), 1–13. https://doi.org/10.1186/1687-417X-2014-1

    Article  Google Scholar 

  2. Holub, V., & Fridrich, J. (2012). Designing steganographic distortion using directional filters. In 2012 IEEE international workshop on information forensics and security (pp. 234–239). https://doi.org/10.1109/WIFS.2012.6412655.

  3. Pevný, T., Filler, T., & Bas, P. (2010). Using high-dimensional image models to perform highly undetectable steganography. In International workshop on information hiding (pp. 161–177).

  4. Xu, G., Wu, H. Z., & Shi, Y. Q. (2016). Structural design of convolutional neural networks for steganalysis. IEEE Signal Processing Letters, 23(5), 708–712. https://doi.org/10.1109/LSP.2016.2548421

    Article  Google Scholar 

  5. Ye, J., Ni, J., & Yi, Y. (2017). Deep learning hierarchical representations for image steganalysis. IEEE Transactions on Information Forensics and Security, 12(11), 2545–2557. https://doi.org/10.1109/TIFS.2017.2710946

    Article  Google Scholar 

  6. Yedroudj, M., Comby, F., & Chaumont, M. (2018). Yedroudj-net: An efficient CNN for spatial steganalysis. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 2092–2096). https://doi.org/10.1109/ICASSP.2018.8461438.

  7. Zhang, R., Zhu, F., Liu, J., & Liu, G. (2020). Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis. IEEE Transactions on Information Forensics and Security, 15, 1138–1150. https://doi.org/10.1109/TIFS.2019.2936913

    Article  Google Scholar 

  8. Zou, D., Shi, Y. Q., Su, W., & Xuan, G. (2006). Steganalysis based on Markov model of thresholded prediction-error image. In 2006 IEEE international conference on multimedia and expo (pp. 1365–1368). https://doi.org/10.1109/ICME.2006.262792.

  9. Pevny, T., Bas, P., & Fridrich, J. (2010). Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on Information Forensics and Security, 5(2), 215–224. https://doi.org/10.1109/TIFS.2010.2045842

    Article  Google Scholar 

  10. Holub, V., Fridrich, J., & Denemark, T. (2013). Random projections of residuals as an alternative to co-occurrences in steganalysis. In Media watermarking, security, and forensics 2013 (Vol. 8665, p. 86650L). International Society for Optics and Photonics.

  11. Luo, X., Liu, F., Lian, S., Yang, C., & Gritzalis, S. (2011). On the typical statistic features for image blind steganalysis. IEEE Journal on Selected Areas in Communications, 29(7), 1404–1422. https://doi.org/10.1109/JSAC.2011.110807

    Article  Google Scholar 

  12. Wang, Y., Liu, J., & Zhang, W. (2009). Blind JPEG steganalysis based on correlations of DCT coefficients in multi-directions and calibrations. In 2009 international conference on multimedia information networking and security (Vol. 1, pp. 495–499). https://doi.org/10.1109/MINES.2009.135.

  13. Boroumand, M., Chen, M., & Fridrich, J. (2019). Deep residual network for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 14(5), 1181–1193. https://doi.org/10.1109/TIFS.2018.2871749

    Article  Google Scholar 

  14. Kodovský, J., Sedighi, V., & Fridrich, J. (2014). Study of cover source mismatch in steganalysis and ways to mitigate its impact. In Media watermarking, security, and forensics 2014 (Vol. 9028, p. 90280J). International Society for Optics and Photonics. https://doi.org/10.1117/12.2039693.

  15. Giboulot, Q., Cogranne, R., Borghys, D., & Bas, P. (2020). Effects and solutions of cover-source mismatch in image steganalysis. Signal Processing: Image Communication, 86, 115888. https://doi.org/10.1016/j.image.2020.115888

    Article  Google Scholar 

  16. Jia, J., Zhai, L., Ren, W., Wang, L., Ren, Y., & Zhang, L. (2020). Transferable heterogeneous feature subspace learning for JPEG mismatched steganalysis. Pattern Recognition, 100, 107105. https://doi.org/10.1016/j.patcog.2019.107105

    Article  Google Scholar 

  17. Xue, Y., Yang, L., Wen, J., Niu, S., & Zhong, P. (2019). A subspace learning-based method for JPEG mismatched steganalysis. Multimedia Tools and Applications, 78(7), 8151–8166. https://doi.org/10.1007/s11042-018-6719-5

    Article  Google Scholar 

  18. Yang, Y., Kong, X., & Feng, C. (2018). Double-compressed JPEG images steganalysis with transferring feature. Multimedia Tools and Applications, 77(14), 17993–18005. https://doi.org/10.1007/s11042-018-5734-x

    Article  Google Scholar 

  19. Feng, C., Kong, X., Li, M., Yang, Y., & Guo, Y. (2017). Contribution-based feature transfer for JPEG mismatched steganalysis. In 2017 IEEE international conference on image processing (ICIP) (pp. 500–504). https://doi.org/10.1109/ICIP.2017.8296331.

  20. Yang, Y., Kong, X., Wang, B., Ren, K., & Guo, Y. (2019). Steganalysis on Internet images via domain adaptive classifier. Neurocomputing, 351, 205–216. https://doi.org/10.1016/j.neucom.2019.04.025

    Article  Google Scholar 

  21. Zhang, X., Kong, X., Wang, P., Wang, B. (2019). Cover-source mismatch in deep spatial steganalysis. In Proceedings of 18th workshop on digital forensics and watermarking (pp. 71–83). https://doi.org/10.1007/978-3-030-43575-2_6.

  22. Hu, D., Ma, Z., Fan, Y., Zheng, S., Ye, D., & Wang, L. (2019). Study on the interaction between the cover source mismatch and texture complexity in steganalysis. Multimedia Tools and Applications, 78(6), 7643–7666. https://doi.org/10.1007/s11042-018-6497-0

    Article  Google Scholar 

  23. Ozcan, S., & Mustacoglu, A. F. (2018). Transfer learning effects on image steganalysis with pre-trained deep residual neural network model. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2280–2287). https://doi.org/10.1109/BigData.2018.8622437.

  24. El Beji, R., Saidi, M., Hermassi, H., & Rhouma, R. (2018). An improved CNN steganalysis architecture based on “catalyst kernels” and transfer learning. In International conference on digital economy (pp. 119–128). https://doi.org/10.1007/978-3-319-97749-2_9.

  25. Yedroudj, M., Chaumont, M., Comby, F., Oulad Amara, A., & Bas, P. (2020). Pixels-off: Data-augmentation complementary solution for deep-learning steganalysis. In Proceedings of the 2020 ACM workshop on information hiding and multimedia security (pp. 39–48). https://doi.org/10.1145/3369412.3395061.

  26. Long, M., Wang, J., Ding, G., Pan, S. J., & Philip, S. Y. (2013). Adaptation regularization: A general framework for transfer learning. IEEE Transactions on Knowledge and Data Engineering, 26(5), 1076–1089. https://doi.org/10.1109/TKDE.2013.111

    Article  Google Scholar 

  27. Pevny, T., & Fridrich, J. (2007). Merging Markov and DCT features for multi-class JPEG steganalysis. In Security, steganography, and watermarking of multimedia contents IX (Vol. 6505, p. 650503). International Society for Optics and Photonics. https://doi.org/10.1117/12.696774.

  28. Fridrich, J., & Kodovsky, J. (2012). Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 7(3), 868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  29. Kodovský, J., & Fridrich, J. (2009). Calibration revisited. In Proceedings of the 11th ACM workshop on multimedia and security (pp. 63–74). https://doi.org/10.1145/1597817.1597830.

  30. Holub, V., & Fridrich, J. (2015). Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Transactions on Information Forensics and Security, 10(2), 219–228. https://doi.org/10.1109/TIFS.2014.2364918

    Article  Google Scholar 

  31. Lin, Y., Wang, R., Dong, L., Yan, D., & Wang, J. (2021). Tackling the cover source mismatch problem in audio steganalysis with unsupervised domain adaptation. IEEE Signal Processing Letters, 28, 1475–1479.

    Article  Google Scholar 

  32. Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191

    Article  Google Scholar 

  33. Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H. P., Schölkopf, B., & Smola, A. J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, 22(14), 49–57. https://doi.org/10.1093/bioinformatics/btl242

    Article  Google Scholar 

  34. Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474.

  35. Long, M., Cao, Y., Wang, J., & Jordan, M. (2015). Learning transferable features with deep adaptation networks. In International conference on machine learning (pp. 97–105).

  36. Long, M., Zhu, H., Wang, J., & Jordan, M. I. (2017). Deep transfer learning with joint adaptation networks. In International conference on machine learning (pp. 2208–2217).

  37. Long, M., Cao, Z., Wang, J., & Jordan, M. I. (2017). Conditional adversarial domain adaptation. arXiv preprint arXiv:1705.10667.

  38. Pei, Z., Cao, Z., Long, M., & Wang, J. (2018). Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence. (pp. 3934–3941).

  39. Wang, J., Chen, Y., Yu, H., Huang, M., & Yang, Q. (2019). Easy transfer learning by exploiting intra-domain structures. 2019 IEEE international conference on multimedia and expo (ICME) (pp. 1210–1215). https://doi.org/10.1109/ICME.2019.00211.

  40. Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., & He, Q. (2020). Deep subdomain adaptation network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1713–1722. https://doi.org/10.1109/TNNLS.2020.2988928

    Article  Google Scholar 

  41. Denemark, T., Sedighi, V., Holub, V., Cogranne, R., & Fridrich, J. (2014). Selection-channel-aware rich model for steganalysis of digital images. In 2014 IEEE International workshop on information forensics and security (WIFS) (pp. 48–53). https://doi.org/10.1109/WIFS.2014.7084302.

  42. Pasquet, J., Bringay, S., & Chaumont, M. (2014). Steganalysis with cover-source mismatch and a small learning database. In 2014 22nd European signal processing conference (EUSIPCO) (pp. 2425–2429).

  43. Bas, P., Filler, T., & Pevný, T. (2011). “Break our steganographic system”: the ins and outs of organizing BOSS. In International workshop on information hiding (pp. 59–70). https://doi.org/10.1007/978-3-642-24178-9_5.

  44. Schaefer, G., & Stich, M. (2004). UCID: An uncompressed color image database. In Storage and retrieval methods and applications for multimedia 2004 (Vol. 5307, pp. 472–480).

  45. Agustsson, E., & Timofte, R. (2017). Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 126–135).

  46. Huiskes, M. J., & Lew, M. S. (2008). The MIR flickr retrieval evaluation. In Proceedings of the 1st ACM international conference on multimedia information retrieval (pp. 39–43). https://doi.org/10.1145/1460096.1460104.

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their kind suggestions for improving the quality of the paper.

Funding

This work was supported by National Natural Science Foundation of China (61972269, 61902263), and Sichuan Science and Technology Program (2022YFG0320).

Author information

Authors and Affiliations

  1. School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, China

    Lei Zhang, Peisong He & Hongxia Wang

  2. College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Sani M. Abdullahi

Authors
  1. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sani M. Abdullahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peisong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Peisong He or Hongxia Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Zhang, L., Abdullahi, S.M., He, P. et al. Dataset mismatched steganalysis using subdomain adaptation with guiding feature. Telecommun Syst 80, 263–276 (2022). https://doi.org/10.1007/s11235-022-00901-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-022-00901-6

Keywords

Search

Navigation