Skip to main content
Log in

Robust digital color image watermarking based on compressive sensing and DWT

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

Abstract

In this paper, a powerful digital watermarking algorithm derived from compressive sensing and discrete wavelet transform to provide authentication and preserve copyright protection for color images. Initially, host color image is converted into YCbCr. Then Y “luminance image” is decomposed with wavelet transform and its low-frequency subband is selected. The watermark image is divided into non-overlapping blocks which are compressed. Finally, the compressed watermark blocks are embedded in Y. In extracting the watermark, L1 optimization algorithm is utilized. Investigated results convey that the developed algorithm has better performance, high concealment and high durability over various attacks of image.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Algorithm 2
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The dataset utilized is publicly available.

References

  1. Ahmadi S B B, Zhang G, Wei S, Boukela L (2020) An intelligent and blind image watermarking scheme based on hybrid svd transforms using human visual system characteristics. Vis Comput 1–25

  2. Baraniuk R G (2007) Compressive sensing [lecture notes]. IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  3. Brannock E, Weeks M, Harrison R W (2009) The effect of wavelet families on watermarking. J Comput 4(6):554–566

    Article  Google Scholar 

  4. Campbell J Y, Lo A W -C, MacKinlay A C, et al (1997) The econometrics of financial markets, vol 2. Princeton University Press, Princeton

    Book  Google Scholar 

  5. Candès E J, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MathSciNet  Google Scholar 

  6. Donoho D L (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  7. Ernawan F, Kabir M N (2020) A block-based rdwt-svd image watermarking method using human visual system characteristics. Vis Comput 36(1):19–37

    Article  Google Scholar 

  8. Himthani V, Dhaka V S, Kaur M, Singh D, Lee H -N (2022) Systematic survey on visually meaningful image encryption techniques. IEEE Access 10:98360–98373

    Article  Google Scholar 

  9. Hu Y, Lu W, Ma M, Sun Q, Wei J (2022) A semi fragile watermarking algorithm based on compressed sensing applied for audio tampering detection and recovery. Multimed Tools Appl 81(13):17729–17746

    Article  Google Scholar 

  10. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44(13):800–801

    Article  Google Scholar 

  11. Kaur M, Kumar V (2018) Parallel non-dominated sorting genetic algorithm-ii-based image encryption technique. Imaging Sci J 66(8):453–462

    Article  Google Scholar 

  12. Kaur M, Singh S, Kaur M (2021) Computational image encryption techniques: a comprehensive review. Math Probl Eng 2021:1–17

    Google Scholar 

  13. Lang J, Ma C (2022) Novel zero-watermarking method using the compressed sensing significant feature. Multimed Tools Appl 1–17

  14. Lang J, Ma C (2023) Novel zero-watermarking method using the compressed sensing significant feature. Multimed Tools Appl 82(3):4551–4567

    Article  Google Scholar 

  15. Liansheng S, Bei Z, Zhanmin W, Ailing T (2017) An optical color image watermarking scheme by using compressive sensing with human visual characteristics in gyrator domain. Opt Lasers Eng 92:85–93

    Article  Google Scholar 

  16. Liao B, Lv J (2015) A novel watermark embedding scheme using compressive sensing in wavelet domain. Open Cybern System J 9(1):1–6

    Article  Google Scholar 

  17. Liu X, Cao Y, Lu P, Lu X, Li Y (2013) Optical image encryption technique based on compressed sensing and arnold transformation. Optik-Int J Light Electron Opt 124(24):6590–6593

    Article  Google Scholar 

  18. Liu H, Xiao D, Zhang R, Zhang Y, Bai S (2016) Robust and hierarchical watermarking of encrypted images based on compressive sensing. Signal Process: Image Commun 45:41–51

    Google Scholar 

  19. Liu D, Su Q, Yuan Z, Zhang X (2020) A color watermarking scheme in frequency domain based on quaternary coding. Vis Comput 1–14

  20. Neetha K K, Koya A M (2015) A compressive sensing approach to dct watermarking system. In: 2015 International conference on control communication & computing India (ICCC). IEEE, pp 495–500

  21. Thakkar F N, Srivastava V K (2017) A fast watermarking algorithm with enhanced security using compressive sensing and principle components and its performance analysis against a set of standard attacks. Multimed Tools Appl 76(14):15191–15219

    Article  Google Scholar 

  22. Thanki R, Dwivedi V, Borisagar K (2017) A hybrid watermarking scheme with cs theory for security of multimedia data. J King Saud Univ - Comput Inf Sci

  23. Prasanth Vaidya S (2018) Multiple decompositions-based blind watermarking scheme for color images. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 132–143

  24. Prasanth Vaidya S (2018) A blind color image watermarking using brisk features and contourlet transform. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 203–215

  25. Prasanth Vaidya S (2022) Fingerprint-based robust medical image watermarking in hybrid transform. Vis Comput 1–16

  26. Prasanth Vaidya S, Chandra Mouli P V S S R (2015) Adaptive digital watermarking for copyright protection of digital images in wavelet domain. Procedia Comput Sci 58:233–240

    Article  Google Scholar 

  27. Prasanth Vaidya S, Chandra Mouli P V S S R (2017) A robust semi-blind watermarking for color images based on multiple decompositions. Multimed Tools Appl 76(24):25623–25656

    Article  Google Scholar 

  28. Prasanth Vaidya S, Chandra Mouli P V S S R (2018) Adaptive, robust and blind digital watermarking using bhattacharyya distance and bit manipulation. Multimed Tools Appl 77(5):5609–5635

    Article  Google Scholar 

  29. Prasanth Vaidya S, Chandra Mouli P V S S R, Santosh K C (2019) Imperceptible watermark for a game-theoretic watermarking system. Int J Mach Learn Cybern 10(6):1323–1339

    Article  Google Scholar 

  30. Prasanth Vaidya S, Chandra Mouli P V S S R (2020) A robust and blind watermarking for color videos using redundant wavelet domain and svd. In: Smart computing paradigms: new progresses and challenges. Springer, pp 11–17

  31. Prasanth Vaidya S, Rajesh Kandala NVPS, Rajasekhar Reddy N V, Chandra Sekhar Reddy N (2020) Patient data hiding into ecg signal using watermarking in transform domain. Phys Eng Sci Med 43(1):213–226

    Article  Google Scholar 

  32. Setiadi D R I M (2021) Psnr vs ssim: imperceptibility quality assessment for image steganography. Multimed Tools Appl 80(6):8423–8444

    Article  Google Scholar 

  33. Tong D, Ren N, Zhu C (2019) Secure and robust watermarking algorithm for remote sensing images based on compressive sensing. Multimed Tools Appl 78(12):16053–16076

    Article  Google Scholar 

  34. Valenzise G, Tagliasacchi M, Tubaro S, Cancelli G, Barni M (2009) A compressive-sensing based watermarking scheme for sparse image tampering identification. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE, pp 1265–1268

  35. Veena V K, Jyothish Lal G, Vishnu Prabhu S, Sachin Kumar S, Soman K P (2012) A robust watermarking method based on compressed sensing and arnold scrambling. In: 2012 international conference on machine vision and image processing (MVIP). IEEE, pp 105–108

  36. Wang Y -R, Lin W -H, Yang L (2011) An intelligent watermarking method based on particle swarm optimization. Expert Syst Appl 38(7):8024–8029

    Article  Google Scholar 

  37. Yang Z, Sun Q, Qi Y, Li S, Ren F (2022) A hyper-chaotically encrypted robust digital image watermarking method with large capacity using compress sensing on a hybrid domain. Entropy 24(10):1486

    Article  Google Scholar 

  38. Yuan Z, Su Q, Liu D, Zhang X (2020) A blind image watermarking scheme combining spatial domain and frequency domain. Vis Comput 1–15

  39. Zhang R, Xiao D (2022) Double image encryption scheme based on compressive sensing and double random phase encoding. Mathematics 10(8):1242

    Article  Google Scholar 

  40. Zhang X, Ren Y, Feng G, Qian Z (2011) Compressing encrypted image using compressive sensing. In: 2011 Seventh international conference on intelligent information hiding and multimedia signal processing (IIH-MSP). IEEE, pp 222–225

  41. Zhang X, Qian Z, Ren Y, Feng G (2011) Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction. IEEE Trans Inf Forens Secur 6(4):1223–1232

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Prasanth Vaidya.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

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

Vaidya, S.P., Mouli, P.V.S.S. Robust digital color image watermarking based on compressive sensing and DWT. Multimed Tools Appl 83, 3357–3371 (2024). https://doi.org/10.1007/s11042-023-15349-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15349-2

Keywords

Navigation