Skip to main content
SpringerLink
Log in

Reversible watermarking based on extreme prediction using modified differential evolution

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Prediction-error expansion (PEE) is an important technique for reversible watermarking (RW). Traditional PEE methods exploit pixel correlations by modifying prediction-error histogram (PEH) with Laplacian-like distributions, but they rarely consider the influence of needless shifting on image distortion. Asymmetric PEE approaches utilize skewed PEHs to select fewer shifted pixels for superior performance in capacity-distortion control tasks. In this way, the construction of a suitable extreme predictor is especially useful for increasing embedding capacity and reducing image distortion to satisfy user requirements. However, in recent works, the parameters of extreme predictors were not fully optimized. This paper presents a novel extreme predictor, in which the prediction parameters are optimized by using a differential evolution (DE) algorithm to increase the performance of asymmetric PEE by building a sharp and skewed PEH. Specifically, we develop a new DE algorithm based on hybrid mutation and grouping crossover operations to further improve the optimization accuracy of the algorithm. Then, an RW scheme based on the proposed predictor and image separation is built to fully exploit the embedding potential of different bit-planes in images. To reduce image distortion, a strategy that is complementary to double-embedding is presented for lower bit-plane (LBP) embedding. Experimental results demonstrate that the proposed scheme can achieve better image quality and a larger embedding capacity than similar state-of-the-art 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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Weng SM, Shi YQ, Hong W, Yao Y (2019) Dynamic improved pixel value ordering reversible data hiding. Inf Sci 489:136–154. https://doi.org/10.1016/j.ins.2019.03.032

    Article  Google Scholar 

  2. Luo L, Chen Z, Chen M, Zeng X, Xiong Z (2010) Reversible image watermarking using interpolation technique. IEEE Trans Inf Forensic Secur 5(1):187–193. https://doi.org/10.1109/TIFS.2009.2035975

    Article  Google Scholar 

  3. Parah SA, Sheikh JA, Akhoon JA, Loan NA (2020) Electronic health record hiding in images for smart city applications: a computationally efficient and reversible information hiding technique for secure communication. Futur Gener Comput Syst 108:935–949. https://doi.org/10.1016/j.future.2018.02.023

    Article  Google Scholar 

  4. Bhardwaj R, Aggarwal A (2021) An enhanced separable reversible and secure patient data hiding algorithm for telemedicine applications. Expert Syst Appl 186:115721. https://doi.org/10.1016/j.eswa.2021.115721

    Article  Google Scholar 

  5. Carpentieri B, Castiglione A, De santis A, Palmieri F, Pizzolante R (2019) One-pass lossless data hiding and compression of remote sensing data. Fut Gener Comput Syst 90:222–239. https://doi.org/10.1016/j.future.2018.07.051

    Article  Google Scholar 

  6. Qin C, Jiang C, Mo Q, Yao H, Chang CC (2021) Reversible data hiding in encrypted image via secret sharing based on GF(p) and GF(28). IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2021.3091319

  7. Puteaux P, Puech W (2021) A recursive reversible data hiding in encrypted images method with a very high payload. IEEE Trans Multimed 23:636–650. https://doi.org/10.1109/TMM.2020.2985537

    Article  Google Scholar 

  8. Wang X, Chang CC, Lin CC (2021) Reversible data hiding in encrypted images with block-based adaptive MSB encoding. Inf Sci 567:375–394. https://doi.org/10.1016/j.ins.2021.02.079

    Article  MathSciNet  Google Scholar 

  9. Thodi DM, Rodriguez JJ (2007) Expansion embedding techniques for reversible watermarking. IEEE Trans Image Process 16(3):721–730. https://doi.org/10.1109/TIP.2006.891046

    Article  MathSciNet  Google Scholar 

  10. Roy A, Chakraborty RS (2020) Toward optimal prediction error expansion-based reversible image watermarking. IEEE Trans Circ Syst Video Technol 30(8):2377–2390. https://doi.org/10.1109/TCSVT.2019.2911042

    Article  Google Scholar 

  11. Fallahpour M (2008) Reversible image data hiding based on gradient adjusted prediction. IEICE Electron Express 5(20):870–876. https://doi.org/10.1587/elex.5.870

    Article  Google Scholar 

  12. Coltuc D (2011) Improved embedding for prediction-based reversible watermarking. IEEE Trans Inf Forensic Secur 6(3):873–882. https://doi.org/10.1109/TIFS.2011.2145372

    Article  Google Scholar 

  13. Sachnev V, Kim HJ, Nam J, Suresh S, Shi YQ (2009) Reversible watermarking algorithm using sorting and prediction. IEEE Trans Circ Syst Video Technol 19 (7):989–999. https://doi.org/10.1109/TCSVT.2009.2020257

    Article  Google Scholar 

  14. Ou B, Li X, Zhao Y, Ni R (2013) Reversible data hiding based on pde predictor. J Syst Softw 86(10):2700–2709. https://doi.org/10.1016/j.jss.2013.05.077

    Article  Google Scholar 

  15. Dragoi I, Coltuc D (2014) Local-prediction-based difference expansion reversible watermarking. IEEE Trans Image Process 23(4):1779–1790. https://doi.org/10.1109/TIP.2014.2307482

    Article  MathSciNet  Google Scholar 

  16. Zheng H, Wang C, Wang J, Xiang S (2019) A new reversible watermarking scheme using the content-adaptive block size for prediction. Sign Process 164:74–83. https://doi.org/10.1016/j.sigpro.2019.05.035

    Article  Google Scholar 

  17. Hwang HJ, Kim S, Kim HJ (2016) Reversible data hiding using least square predictor via the lasso. EURASIP J Image Video Process 42:1687–5281. https://doi.org/10.1186/s13640-016-0144-3

    Google Scholar 

  18. Wang W, Wang C, Zheng H, Wang J, Xiao D (2020) An improved reversible watermarking scheme using weighted prediction and watermarking simulation. Sign Process Image Commun 81:115705. https://doi.org/10.1016/j.image.2019.115705

    Article  Google Scholar 

  19. Chen X, Sun X, Sun H, Zhou Z, Zhang J (2013) Reversible watermarking method based on asymmetric-histogram shifting of prediction errors. J Syst Softw 86:2620–2626. https://doi.org/10.1016/j.jss.2013.04.086

    Article  Google Scholar 

  20. Kim S, Qu X, Sachnev V, Kim HJ (2019) Skewed histogram shifting for reversible data hiding using a pair of extreme predictions. IEEE Trans Circ Syst Video Technol 29(11):3236–3246. https://doi.org/10.1109/TCSVT.2018.2878932

    Article  Google Scholar 

  21. Wang W, Ye J, Wang T, Wang W (2017) Reversible data hiding scheme based on significant-bit-difference expansion. IET Image Process 11:1002–1014. https://doi.org/10.1049/iet-ipr.2017.0151

    Article  Google Scholar 

  22. Kumar R, Jung KH (2020) Robust reversible data hiding scheme based on two-layer embedding strategy. Inf Sci 512:96–107. https://doi.org/10.1016/j.ins.2019.09.062

    Article  MathSciNet  Google Scholar 

  23. Chang CC, Kieu TD (2010) A reversible data hiding scheme using complementary embedding strategy. Inf Sci 180(16):3045–3058. https://doi.org/10.1016/j.ins.2010.03.027

    Article  Google Scholar 

  24. Lu TC, Tseng CY, Wu JH (2016) Asymmetric-histogram based reversible information hiding scheme using edge sensitivity detection. J Syst Softw 116:2–21. https://doi.org/10.1016/j.jss.2015.04.085

    Article  Google Scholar 

  25. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

  26. Ali M, Ahn CW (2014) An optimized watermarking technique based on self-adaptive de in dwt–svd transform domain. Signal Process 94:545–556. https://doi.org/10.1016/j.sigpro.2013.07.024

    Article  Google Scholar 

  27. Lei B, Tan EL, Chen S, Ni D, Wang T, Lei H (2014) Reversible watermarking scheme for medical image based on differential evolution. Expert Syst Appl 41 (7):3178–3188. https://doi.org/10.1016/j.eswa.2013.11.019

    Article  Google Scholar 

  28. Vali MH, Aghagolzadeh A, Baleghi Y (2018) Optimized watermarking technique using self-adaptive differential evolution based on redundant discrete wavelet transform and singular value decomposition. Expert Syst Appl 114:296–312. https://doi.org/10.1016/j.eswa.2018.07.004

    Article  Google Scholar 

  29. Salimi L, Haghighi A, Fathi A (2020) A novel watermarking method based on differential evolutionary algorithm and wavelet transform. Multimed Tools Appl:1–18

  30. Yi S, Zhou Y (2018) Parametric reversible data hiding in encrypted images using adaptive bit-level data embedding and checkerboard based prediction. Sign Process 150:171–182. https://doi.org/10.1016/j.sigpro.2018.04.016

    Article  Google Scholar 

  31. Zhou S, Zhang W, Shen C (2020) Rate-distortion model for grayscale-invariance reversible data hiding. Sign Process 107562:172. https://doi.org/10.1016/j.sigpro.2020.107562

    Google Scholar 

  32. Chen X (2020) Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects. Energy 117874:203. https://doi.org/10.1016/j.energy.2020.117874

    Google Scholar 

  33. Loshchilov I, Hutter F (2017) Sgdr: Stochastic gradient descent with warm restarts. 5th International Conference on Learning Representations (ICLR 2017)

  34. Khalfi S, Draa A, Iacca G (2021) A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments. Expert Syst Appl 115705:186. https://doi.org/10.1016/j.eswa.2021.115705

    Google Scholar 

  35. Xia X, Tong L, Zhang Y, Xu X, Yang H, Gui L, Li Y, Li K (2021) NFDDE: A novelty-hybrid-fitness driving differential evolution algorithm. Inf Sci 579:33–54. https://doi.org/10.1016/j.ins.2021.07.082

    Article  MathSciNet  Google Scholar 

  36. Li Y, Wang S (2019) Differential evolution algorithm with elite archive and mutation strategies collaboration. Artif Intell Rev:1–46

  37. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657. https://doi.org/10.1109/TEVC.2006.872133

    Article  Google Scholar 

  38. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on evolutionary computation, pp 71–78. https://doi.org/10.1109/CEC.2013.6557555

  39. He X, Zhou Y (2018) Enhancing the performance of differential evolution with covariance matrix self-adaptation. Appl Soft Comput 64:227–243. https://doi.org/10.1016/j.asoc.2017.11.050

    Article  Google Scholar 

  40. Yu X, Li C, Zhao WX, Chen H (2020) A novel case adaptation method based on differential evolution algorithm for disaster emergency. Appl Soft Comput 92:106306. https://doi.org/10.1016/j.asoc.2020.106306

    Article  Google Scholar 

  41. Deng LB, Li CL, Sun GJ (2020) An adaptive dimension level adjustment framework for differential evolution. Knowl-Based Syst 206:106388. https://doi.org/10.1016/j.knosys.2020.106388

    Article  Google Scholar 

  42. Lin SL, Huang CF, Liou MH, Chen CY (2013) Improving histogram-based reversible information hiding by an optimal weight-based prediction scheme. J Inf Hiding Multimed Signal Process 4(1):19–33

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Hainan Province Basic and Applied Basic Research Program High-level Talent Project (No. 2019RC044) and the Research Project of Education and Teaching Reform of Hainan University (No. hdjy2053).

Author information

Authors and Affiliations

  1. School of Cyberspace Security, Hainan University, Haikou, 570228, China

    Yujian Zhuang, Sheng Liu, Changfeng Ding & Xiaoyi Zhou

Authors
  1. Yujian Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changfeng Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyi Zhou.

Ethics declarations

Conflict of Interests

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

Zhuang, Y., Liu, S., Ding, C. et al. Reversible watermarking based on extreme prediction using modified differential evolution. Appl Intell 52, 14406–14425 (2022). https://doi.org/10.1007/s10489-022-03211-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03211-1

Keywords

Search

Navigation