Skip to main content
SpringerLink
Log in

To deliver more information in coverless information hiding

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Coverless information hiding realizes the hiding of secret messages without modifying the carrier, so being able to resist steganalysis algorithms. Many existing hiding methods, however, still face the dilemma of low hiding capacity (i.e., the volume of hidden messages by a single carrier is bounded), which places some restrictions on the application of the methods. To alleviate this problem, in this paper, we explore an effective coverless information hiding method that can deliver more messages through a cover image. A key step of our method is to construct a visual dictionary based on the bag-of-words model. Utilizing the visual dictionary, one participant can generate a binary coding matrix corresponding to the cover image, and then convert the secret messages, based on the matrix, into a coordinate sequence. To enhance the security, the coordinate sequence is encrypted and the ciphertext is transmitted to another participant together with the cover image. Some experiments are implemented to verify the effectiveness of our method, the results and analysis show that our method performs well in terms of undetectability, hiding capacity, and robustness. Compared with other coverless information hiding methods, our method does not need to construct and maintain a large-scale image library and can deliver more messages by only one single image. These characteristics demonstrate that our method has certain application value, and it also broadens the research ideas of coverless information hiding methods.

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

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Code Availability

The code used to support the findings of this study is available from the corresponding author upon request.

Notes

  1. The Caltech101 dataset is available at http://www.vision.caltech.edu/Image_Datasets/Caltech101/.

  2. The holiday dataset is available at http://lear.inrialpes.fr/~jegou/data.php#holidays.

References

  1. Abdulsattar FS (2021) Towards a high capacity coverless information hiding approach. Multimed Tools Applic 80(12):18821–18837

    Article  Google Scholar 

  2. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the international conference on machine learning. PMLR, pp 214–223

  3. Cao Y, Zhou Z, Wu QM, Yuan C, Sun X (2020) Coverless information hiding based on the generation of anime characters. EURASIP J Image Vid Process 2020(1):1–15

    Google Scholar 

  4. Chen X, Sun H, Tobe Y, Zhou Z, Sun X (2015) Coverless information hiding method based on the chinese mathematical expression. In: Proceedings of the international conference on cloud computing and security. Springer, pp 133–143

  5. Dong T, Li X, Yao H, Qin C (2021) Robust coverless information hiding based on image classification. J Appl Sci 39(6):893–905

    Google Scholar 

  6. Duan X, Song H (2018) Coverless information hiding based on generative model. arXiv:1802.03528

  7. Duan X, Li B, Guo D, Zhang Z, Ma Y (2020) A coverless steganography method based on generative adversarial network. EURASIP J Image Vid Process 2020(1):1–10

    Google Scholar 

  8. Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceedings of the 2004 conference on computer vision and pattern recognition workshop. IEEE, pp 178–178

  9. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in Neural Information Processing Systems, 27

  10. Hartigan J A, Wong M A (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Series C (Appl Stat) 28(1):100–108

    Google Scholar 

  11. Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the European conference on computer vision. Springer, pp 304–317

  12. Kadhim I J, Premaratne P, Vial P J, Halloran B (2019) Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing 335:299–326

    Article  Google Scholar 

  13. Li Q, Wang X, Wang X, Shi Y (2021) Cccih: content-consistency coverless information hiding method based on generative models. Neural Process Lett 53(6):4037–4046

    Article  Google Scholar 

  14. Liu Q, Xiang X, Qin J, Tan Y, Qiu Y (2020) Coverless image steganography based on densenet feature mapping. EURASIP J Image Vid Process 2020(1):1–18

    Google Scholar 

  15. Lowe D G (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  16. Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135

    Article  Google Scholar 

  17. Muhuri P K, Ashraf Z, Goel S (2020) A novel image steganographic method based on integer wavelet transformation and particle swarm optimization. Appl Soft Comput 92:106257

    Article  Google Scholar 

  18. Sahu A K, Swain G (2019) A novel n-rightmost bit replacement image steganography technique. 3D Res 10(1):1–18

    Article  Google Scholar 

  19. Sahu A K, Swain G (2020) Reversible image steganography using dual-layer lsb matching. Sens Imag 21(1):1–21

    Article  Google Scholar 

  20. Yang J, Jiang Y-G, Hauptmann A G, Ngo C-W (2007) Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the international workshop on workshop on multimedia information retrieval, pp 197–206

  21. Yang L, Deng H, Dang X (2020) A novel coverless information hiding method based on the most significant bit of the cover image. IEEE Access 8:108579–108591

    Article  Google Scholar 

  22. Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on sift and bof. J Internet Technol 18(2):435–442

    Google Scholar 

  23. Zhang X, Peng F, Lin Z, Long M (2020) A coverless image information hiding algorithm based on fractal theory. Int J Bifur Chaos 30(04):2050062

    Article  MathSciNet  Google Scholar 

  24. Zheng W, Wang K, Wang F-Y (2020) Gan-based key secret-sharing scheme in blockchain. IEEE Trans Cybern 51(1):393–404

    Article  Google Scholar 

  25. Zhou Z, Sun H, Harit R, Chen X, Sun X (2015) Coverless image steganography without embedding. In: Proceedings of the international conference on cloud computing and security. Springer, pp 123–132

  26. Zou L, Sun J, Gao M, Wan W, Gupta B B (2019) A novel coverless information hiding method based on the average pixel value of the sub-images. Multimed Tools Applic 78(7):7965–7980

    Article  Google Scholar 

Download references

Funding

This article is funded by the National Natural Science Foundation of China (62262062).

Author information

Authors and Affiliations

  1. School of Information Engineering, Xizang Minzu University, Xianyang, 712000, Shaanxi, China

    Hailun Liu, Chunyu Zhang, Chenfei Guo, Peidong Gou, Liying Shan & Zewei Lu

  2. School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China

    Zhaojie Wang

Authors
  1. Hailun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaojie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenfei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peidong Gou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liying Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zewei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

1. Conceptualization and methodology: Hailun Liu, Chunyu Zhang, Zhaojie Wang.

2. Validation: Hailun Liu, Chunyu Zhang, Zhaojie Wang.

3. Capacity and undetectability analysis: Peidong Gou, Liying Shan, Zewei Lu.

4. Robustness analysis: Hailun Liu, Chunyu Zhang, Chenfei Guo.

5. Writing-original draft preparation: Hailun Liu, Zhaojie Wang.

6. Writing-review and editing: Hailun Liu, Chunyu Zhang, Zhaojie Wang.

Corresponding author

Correspondence to Chunyu Zhang.

Ethics declarations

Ethics approval

Approval

Consent for publication

All authors consent for publication.

Consent to participate

All authors consent to participate.

Conflict of Interests

The authors declare no conflict of interest/competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Zhang, C., Wang, Z. et al. To deliver more information in coverless information hiding. Multimed Tools Appl 83, 7215–7229 (2024). https://doi.org/10.1007/s11042-023-15263-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15263-7

Keywords

Search

Navigation