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A watermarking algorithm in encrypted image based on compressive sensing with high quality image reconstruction and watermark performance

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Abstract

In this paper, we propose a new digital watermarking algorithm in encrypted image based on compressive sensing measurements and 2-D discrete wavelet transform (DWT). Firstly, we process the original image through 2-D DWT to highlight the important part and unimportant part. For the important LL2 coefficient, before encrypting it by the traditional stream cipher, we divide it into blocks, and mark the blocks to get a sequence as the watermark position key. For other wavelet coefficients, we select two different compressive sensing measurement matrices to simultaneously encrypt and compress them, respectively. Then we embed a watermark into the high frequency coefficient measurements except HH1 section based on the watermark position key. Finally, the watermarked image is scrambled to enhance the security. In this algorithm, compressive sensing is adopted for compression and encryption, and watermark is embedded in the measurements values. It can not only increase the watermark embedding capacity and robustness, but also utilize the reconstruction characteristic of compressive sensing to get higher-quality recovered image. The experimental results verify the validity and the reliability of the proposed algorithm.

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References

  1. Borges PVK, Mayer J, Izquierdo E (2008) Robust and transparent color modulation for text data hiding. IEEE Trans Multimedia 10(8):1479–1489

    Article  Google Scholar 

  2. Butler-Purry KL, Bagriyanik M (2003) Characterization of transients in transformers using discrete wavelet transforms. IEEE Trans Power Syst 18(2):648–656

    Article  Google Scholar 

  3. Candes E, 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  MATH  Google Scholar 

  4. Chang C-L, Girod B (2007) Direction-adaptive discrete wavelet transform for image compression. IEEE Trans Image Process 16(5):1289–1302

    Article  MathSciNet  Google Scholar 

  5. Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by Basis Pursuit. J Sci Comput 20(1):33–61

    MathSciNet  MATH  Google Scholar 

  6. Chen G, Guo S, Li Y (2012) Digital image watermarking algorithm based on compressive sensing. Mod Electron Technol 35(13):98–104

    Google Scholar 

  7. Davenport MA, TBoufounos P, Wakin MB, Baraniuk RG (2010) Signal processing with compressive measurements. IEEE J Sel Top Sign Proces 4(2):445–460

    Article  Google Scholar 

  8. Donoho D (2006) Compressive sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  9. Hong W, Chen TS, Wu HY (2012) An improved reversible data hiding in encrypted images using side match. IEEE Signal Process Lett 19(4):199–202

    Article  Google Scholar 

  10. Hossein SA, Tabatabaei AE, Eivie N (2012) Security analysis of the joint encryption and compressive sensing. IEEE 20th Telecommunication Forum: 799–802

  11. Huang HC (2014) Robust image watermarking based on compressed sensing techniques. J Inf Hiding Multimed Signal Process 5(2):2073–4212

    Google Scholar 

  12. Huang HC, Chang FC, Wu CH (2012) Watermarking for compressive sampling applications. 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP): 223–226

  13. Kalra GS, Talwar R, Sadawarti H (2014) Adaptive digital image watermarking for color images in frequency domain. Multimed Tools Appl. doi:10.1007/s11042-014-1932-3

    Google Scholar 

  14. Karim MSA, Wong K (2014) Universal data embedding in encrypted domain. Signal Process 94:174–182

    Article  Google Scholar 

  15. Lin T-C, Lin C-M (2009) Wavelet-based copyright -protection scheme for digital images based on local features. Inf Sci 179(19):3349–3358

    Article  Google Scholar 

  16. Liu K, Kargupta H, Ryan J (2006) Random projection based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans Knowl Data Eng 18(1):92–106

    Article  Google Scholar 

  17. Lu T-C, Chang C-C, Huang Y-H (2014) High capacity reversible hiding scheme based on interpolation, difference expansion, and histogram shifting. Multimed Tools Appl 72(1):417–435

    Article  Google Scholar 

  18. Malik HMA, Ansari R, Khokhar AA (2007) Robust data hiding in audio using all pass filters. IEEE Trans Audio Speech Lang Process 15(4):1296–1304

    Article  Google Scholar 

  19. Pan J-S, Li W, Yang C, Yan L-J (2014) Image steganography based on sub-sampling and compressive sensing. Multimed Tools Appl. doi:10.1007/s11042-014-2076-1

    Google Scholar 

  20. Qian ZX, Zhang XP, Ren YL, Feng GR (2015) Block cipher based separable reversible data hiding in encrypted images. Multimed Tools Appl. doi:10.1007/s11042-015-2760-9

    Google Scholar 

  21. Rachlin Y, Baron D (2008) The secrecy of compressive sensing measurements. The 46th Allerton Conference on Communication, Control and Computing: 813–817

  22. Sun R, Zeng W (2014) Secure and robust image hashing via compressive sensing. Multimed Tools Appl 70(3):1651–1665

    Article  Google Scholar 

  23. Van De Ville D, Philips W, Van de Walle R, Lemahieu I (2004) Image scrambling without band width expansion. IEEE Trans Circuits Syst Video Technol 14(6):892–897

    Article  Google Scholar 

  24. Veena VK, Jyothish Lal G, Prabhu S, Sachin Kumar S(2012) The secrecy of compressive sensing measurements. International Conference on Machine Vision and Image Processing (MVIP) 105–108

  25. Wei F, Liang D, Zhang C, Bao W (2013) Digital image watermarking algorithm based on compressed sensing observation value. J Anhui Univ 37(3):61–68 (In Chinese)

    Google Scholar 

  26. Wong K, Tanaka K, Takagi K, Nakajima Y (2009) Complete video quality- preserving data hiding. IEEE Trans Circuits Syst Video Technol 19(10):1499–1512

    Article  Google Scholar 

  27. Xiao D, Chen S (2014) Separable data hiding in encrypted image based on compressive sensing. Electron Lett 5(8):598–600

    Article  Google Scholar 

  28. Zhang XP (2011) Reversible data hiding in encrypted images. IEEE Trans Inf Forensics Secur 18(4):255–258

    Google Scholar 

  29. Zhang XP (2012) Separable reversible data hiding in encrypted image. IEEE Trans Inf Forensics Secur 7(2):826–832

    Article  Google Scholar 

  30. Zhang H (2012) Applications research of compressed sensing in information hiding. Dissertation. Northern Industrial University

  31. Zhang J, Xia L, Huang M, Li G (2014) Image reconstruction in compressed sensing based on single-level DWT. IEEE Workshop on Electronics, Computer and Applications: 941–944

  32. Zhao C, Liu W (2012) Interactive support and dual watermarking algorithm based on compressive sensing. Electron J 40(40):681–687

    Google Scholar 

  33. Zhao C, Liu W (2012) A semi-fragile zero watermarking image algorithm based on block compressive sensing. Autom J 38(4):609–617

    MATH  Google Scholar 

  34. Zhou L, Wu D, Zheng B, Guizani M (2014) Joint physical-application layer security for wireless multimedia delivery. IEEE Commun Mag 52(3):66–72

    Article  Google Scholar 

Download references

Acknowledgments

The work described in this paper was funded by the National Natural Science Foundation of China (Grant Nos. 61272043, 61302161, 61472464, 61502399, 61572089), the Natural Science Foundation of Chongqing Science and Technology Commission (Grant Nos. cstc2012jjA40017, cstc2013jcyjA40017, cstc2013jjB40009, cstc2015jcyjA40039) and the Fundamental Research Funds for the Central Universities (Grant Nos. 106112013CDJZR180005, 106112014CDJZR185501).

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Authors and Affiliations

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, College of Computer Science, Chongqing University, Chongqing, 400044, China

    Di Xiao, Yanting Chang & Tao Xiang

  2. Department of Information Engineering, Chongqing Communication Institute, Chongqing, China

    Sen Bai

Authors
  1. Di Xiao
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  2. Yanting Chang
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  3. Tao Xiang
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  4. Sen Bai
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Correspondence to Di Xiao.

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Xiao, D., Chang, Y., Xiang, T. et al. A watermarking algorithm in encrypted image based on compressive sensing with high quality image reconstruction and watermark performance. Multimed Tools Appl 76, 9265–9296 (2017). https://doi.org/10.1007/s11042-016-3532-x

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  • DOI: https://doi.org/10.1007/s11042-016-3532-x

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