Skip to main content
SpringerLink
Log in

A novel robust image watermarking in quaternion wavelet domain based on superpixel segmentation

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Feature-based image watermarking is a well-developed area of digital image watermarking. The key advantage of such an approach is the ability to guard against the local desynchronization attacks. However, research in this area usually suffers from the following limitations: (1) The feature points focus too much on the high contrast region and are distributed unevenly. (2) The feature point detector is sensitive to the texture region, and some noise feature points are always detected in the texture region. Based on superpixel image segmentation and quaternion wavelet transform, we propose a digital image watermarking approach which is highly robust against common image processing operations and local geometric transformations. First, the entropy rate superpixel segmentation is used to segment the host image into several homogeneous regions. Then, for each segment region, image feature points are extracted using the SIFER detector, and the affine invariant local regions are constructed adaptively. Finally, the digital watermark is embedded into the local regions by modulating the invariant modulus coefficients. Experimental results are provided to illustrate the efficiency of the proposed image watermarking, especially for noise attacks and local desynchronization attacks.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Bollimpalli, P., Sahu, N., & Sur, A. (2015). SIFT based robust image watermarking resistant to resolution scaling. In IEEE International Conference on Image Processing (pp. 5507–5511).

  • Boulkenafet, Z., Komulainen, J., & Hadid, A. (2016). Face anti-spoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Processing Letters, 24(2), 141–145.

    Google Scholar 

  • Deng, C., Li, J., & Gao, X. (2010). Geometric attacks resistant image watermarking in affine covariant regions. Acta Automatic Sinica, 26(2), 221–228.

    Article  Google Scholar 

  • Fletcher, P., & Sangwine, S. J. (2017). The development of the quaternion wavelet transform. Signal Processing, 136, 2–15.

    Article  Google Scholar 

  • Guo, Y., Li, B. Z., & Goel, N. (2017). Optimised blind image watermarking method based on firefly algorithm in DWT-QR transform domain. IET Image Processing, 11(6), 406–415.

    Article  Google Scholar 

  • Hua, G., Goh, J., & Thing, L. L. (2015). Time-spread echo-based audio watermarking with optimized imperceptibility and robustness. IEEE Transactions on Audio Speech and Language Processing, 23(2), 227–239.

    Article  Google Scholar 

  • Hua, G., Huang, J., Shi, Y. Q., et al. (2016). Twenty years of digital audio watermarking-a comprehensive review. Signal Processing, 128, 222–242.

    Article  Google Scholar 

  • Isa, M., Aliareh, S., & Yusoff, Z. (2017). A watermarking technique to improve the security level in face recognition systems. Multimedia Tools and Applications, 76(22), 23805–23833.

    Article  Google Scholar 

  • Kaur, A., & Dutta, M. K. (2018). An optimized high payload audio watermarking algorithm based on LU-factorization. Multimedia Systems, 24(3), 341–353.

    Article  Google Scholar 

  • Kuribayashi, M., & Shigemoto, S. (2017). DCT-OFDM watermarking scheme based on communication system model. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E100-A(4), 944–952.

    Article  Google Scholar 

  • Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., et al. (2009). TurboPixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2290–2297.

    Article  Google Scholar 

  • Li, C., Li, J., & Fu, B. (2013). Magnitude-phase of quaternion wavelet transform for texture representation using multilevel copula. IEEE Signal Processing Letters, 20(8), 799–802.

    Article  Google Scholar 

  • Lin, B., Wang, F., Zhao, F., et al. (2018). Scale invariant point feature (SIPF) for 3D point clouds and 3D multi-scale object detection. Neural Computing and Applications, 29(5), 1209–1224.

    Article  Google Scholar 

  • Liu, M., Tuzel, O., & Ramalingam, S. (2011). Entropy rate superpixel segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2097–2104).

  • Long, F., Zhou, B., & Ming, D. (2015). A new method to extract stable feature points based on self-generated simulation images. Image Processing and Analysis, 9765, 95751–95756.

    Google Scholar 

  • Maheshkar, S. (2017). Region-based hybrid medical image watermarking for secure telemedicine applications. Multimedia Tools and Applications, 76(3), 1–31.

    Google Scholar 

  • Mainali, P. (2014). Robust registration in integrated hyperspectral imaging (robuuste registratie in geïntegreerde, hyperspectrale beeldopname). 2014. Web.

  • Mainali, P., Lafruit, G., Yang, Q., et al. (2013). SIFER: Scale-invariant feature detector with error resilience. International Journal of Computer Vision, 104(2), 172–197.

    Article  Google Scholar 

  • Moore, A. P., Prince, S. J. D., & Warrell, J. (2010). Lattice cut-constructing superpixels using layer constraints. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117–2124).

  • Qasim, A. F., Meziane, F., & Aspin, R. (2018). Digital watermarking: Applicability for developing trust in medical imaging workflows state of the art review. Computer Science Review, 27, 45–60.

    Article  MathSciNet  Google Scholar 

  • Shih, F. Y. (2017). Digital watermarking and steganography: Fundamentals and techniques (2nd ed.). Boca Raton: CRC Press.

    Book  Google Scholar 

  • Su, P. C., Kuo, T. Y., & Li, M. H. (2017). A practical design of digital watermarking for video streaming services. Journal of Visual Communication and Image Representation, 42, 161–172.

    Article  Google Scholar 

  • Thien, H. T., Hua, C. H., Tu, N. A., et al. (2018). Selective bit embedding scheme for robust blind color image watermarking. Information Sciences, 426, 1–18.

    Article  Google Scholar 

  • Wang, C., Wang, X., & Zhang, C. (2017). Geometric correction based color image watermarking using fuzzy least squares support vector machine and Bessel K form distribution. Signal Processing, 134, 197–208.

    Article  Google Scholar 

  • Wang, X. Y., Xu, H., Zhang, S. Y., et al. (2018). A color image watermarking approach based on synchronization correction. Fundamenta Informaticae, 158(4), 385–407.

    Article  MathSciNet  Google Scholar 

  • Wang, Y. X., Niu, P. P., Yang, Y. H., et al. (2014). A new robust color image watermarking using local quaternion exponent moments. Information Sciences, 277, 731–754.

    Article  Google Scholar 

  • Wang, Z., Zhang, L., & Fang, T. (2015). A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2409–2425.

    Article  Google Scholar 

  • Xu, Z., & Yuan, W. (2017). Watermark BER and channel capacity analysis for QPSK-based RF watermarking by constellation dithering in AWGN channel. IEEE Signal Processing Letters, 24(7), 1068–1072.

    Article  Google Scholar 

  • Yang, H. Y., Wang, P., Wang, X. Y., et al. (2015). Robust digital watermarking based on local invariant radial harmonic fourier moments. Multimedia Tools and Applications, 74(23), 10559–10579.

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grant (Nos. 61472171, 61701212), Project funded by China Postdoctoral Science Foundation (No. 2018T110220), Key Scientific Research Project of Liaoning Provincial Department (LZ2019001), and Natural Science Foundation of Liaoning Province (2019-ZD-0468).

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Pan-pan Niu, Li Wang, Xin Shen, Si-yu Zhang & Xiang-yang Wang

Authors
  1. Pan-pan Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Si-yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiang-yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pan-pan Niu or Xiang-yang Wang.

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

Niu, Pp., Wang, L., Shen, X. et al. A novel robust image watermarking in quaternion wavelet domain based on superpixel segmentation. Multidim Syst Sign Process 31, 1509–1530 (2020). https://doi.org/10.1007/s11045-020-00718-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-020-00718-z

Keywords

Search

Navigation