Skip to main content
SpringerLink
Log in

Improving Text-Image Matching with Adversarial Learning and Circle Loss for Multi-modal Steganography

  • Conference paper
  • First Online:
Digital Forensics and Watermarking (IWDW 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12617))

Included in the following conference series:

Abstract

This paper proposes a multi-modal steganography method based on an improved text-image matching algorithm. At present, most of the steganography methods are based on single modality of carriers and embed confidential information into the carriers by cover modification or cover synthesis. Since the distortions between the covers after embedding and the original covers are inevitable, these steganography methods can be detected by the existing steganalysis methods. To solve this problem, we propose multi-modal steganography which hides the confidential information in the semantic relevancy between two modalities of original carriers. The semantic relevancy between the two modalities is measured by text-image matching, which affects the imperceptibility of the proposed method to a large extent. In order to increase the security of multi-modal steganography, we improve the current text-image matching algorithm with adversarial learning and circle loss. By selecting and transmitting the original multi-modal carriers with high relevancy, the proposed method can escape from the detection of current steganalysis methods. It is also illustrated by the theoretical analysis and experimental results that the semantic relevancy between the selected multi-modal carriers is enhanced.

This research is supported by the National Key R&D Program (2018YFB0804103) and the National Natural Science Foundation of China (No. U1705261 and No. U1836204).

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. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  2. Du, Y., Yin, Z., Zhang, X.: Improved lossless data hiding for JPEG images based on histogram modification. Comput. Mater. Continua 55(3), 495–507 (2018)

    Google Scholar 

  3. Duan, X., Song, H., Qin, C., Khan, M.K.: Coverless steganography for digital images based on a generative model. Comput. Mater. Continua 55(3), 483–493 (2018)

    Google Scholar 

  4. Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: improving visual-semantic embeddings with hard negatives. arXiv preprint arXiv:1707.05612 (2017)

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

    Article  Google Scholar 

  6. Hu, Y., Li, H., Song, J., Huang, Y.: MM-stega: multi-modal steganography based on text-image matching. In: Sun, X., Wang, J., Bertino, E. (eds.) ICAIS 2020. CCIS, vol. 1254, pp. 313–325. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-8101-4_29

    Chapter  Google Scholar 

  7. Hu, Y., Zheng, L., Yang, Y., Huang, Y.: Twitter100k: a real-world dataset for weakly supervised cross-media retrieval. IEEE Trans. Multimed. 20(4), 927–938 (2018)

    Article  Google Scholar 

  8. Li, L., Zhang, W., Chen, K., Zha, H., Yu, N.: Side channel steganalysis: when behavior is considered in steganographer detection. Multimed. Tools Appl. 78(7), 8041–8055 (2019)

    Article  Google Scholar 

  9. Liu, R., Zhao, Y., Wei, S., Zheng, L., Yang, Y.: Modality-invariant image-text embedding for image-sentence matching. ACM Trans. Multimed. Comput. Commun. Appl. 15(1), 27 (2019)

    Article  Google Scholar 

  10. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  12. Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6398–6407 (2020)

    Google Scholar 

  13. Wu, S., Zhong, S., Liu, Y.: Deep residual learning for image steganalysis. Multimed. Tools Appl. 77(9), 10437–10453 (2017). https://doi.org/10.1007/s11042-017-4440-4

    Article  Google Scholar 

  14. 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 

  15. Yang, Z., Guo, X., Chen, Z., Huang, Y., Zhang, Y.: RNN-stega: linguistic steganography based on recurrent neural networks. IEEE Trans. Inf. Forensics Secur. 14(5), 1280–1295 (2018)

    Article  Google Scholar 

  16. Yang, Z., Huang, Y., Zhang, Y.: A fast and efficient text steganalysis method. IEEE Signal Process. Lett. 26(4), 627–631 (2019)

    Article  Google Scholar 

  17. Yang, Z., Wang, K., Li, J., Huang, Y., Zhang, Y.: TS-RNN: text steganalysis based on recurrent neural networks. IEEE Signal Process. Lett. 26(12), 1743–1747 (2019). https://doi.org/10.1109/LSP.2019.2920452

    Article  Google Scholar 

  18. Yang, Z., Zhang, S., Hu, Y., Hu, Z., Huang, Y.: VAE-stega: linguistic steganography based on variational auto-encoder. IEEE Trans. Inf. Forensics Secur. 16, 880–895 (2020)

    Article  Google Scholar 

  19. Zhang, Y., Ye, D., Gan, J., Li, Z., Cheng, Q.: An image steganography algorithm based on quantization index modulation resisting scaling attacks and statistical detection. Comput. Mater. Continua 56(1), 151–167 (2018)

    Article  Google Scholar 

  20. Zheng, S., Wang, L., Ling, B., Hu, D.: Coverless information hiding based on robust image hashing. In: Huang, D.-S., Hussain, A., Han, K., Gromiha, M.M. (eds.) ICIC 2017. LNCS (LNAI), vol. 10363, pp. 536–547. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63315-2_47

    Chapter  Google Scholar 

  21. Zhou, Z., Mu, Y., Wu, Q.M.J.: Coverless image steganography using partial-duplicate image retrieval. Soft. Comput. 23(13), 4927–4938 (2018). https://doi.org/10.1007/s00500-018-3151-8

    Article  Google Scholar 

  22. Zhou, Z., Sun, H., Harit, R., Chen, X., Sun, X.: Coverless image steganography without embedding. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds.) ICCCS 2015. LNCS, vol. 9483, pp. 123–132. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27051-7_11

    Chapter  Google Scholar 

  23. Zhou, Z., Wu, Q.J., Yang, C.N., Sun, X., Pan, Z.: Coverless image steganography using histograms of oriented gradients-based hashing algorithm. J. Internet Technol. 18(5), 1177–1184 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Yuting Hu, Han Cao, Zhongliang Yang & Yongfeng Huang

  2. Beijing National Research Center for Information Science and Technology, Beijing, 100084, China

    Yuting Hu, Zhongliang Yang & Yongfeng Huang

Authors
  1. Yuting Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhongliang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongfeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongliang Yang .

Editor information

Editors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA

    Yun-Qing Shi

  3. Department of Information Engineering, University of Florence, Florence, Italy

    Alessandro Piva

  4. School of Cybersecurity, Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hu, Y., Cao, H., Yang, Z., Huang, Y. (2021). Improving Text-Image Matching with Adversarial Learning and Circle Loss for Multi-modal Steganography. In: Zhao, X., Shi, YQ., Piva, A., Kim, H.J. (eds) Digital Forensics and Watermarking. IWDW 2020. Lecture Notes in Computer Science(), vol 12617. Springer, Cham. https://doi.org/10.1007/978-3-030-69449-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69449-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69448-7

  • Online ISBN: 978-3-030-69449-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation