Multimedia Tools and Applications

, Volume 78, Issue 12, pp 16053–16076 | Cite as

Secure and robust watermarking algorithm for remote sensing images based on compressive sensing

  • Deyu Tong
  • Na RenEmail author
  • Changqing Zhu


The aim of this paper is to improve the reconstruction accuracy and security when adopting Compressive Sensing (CS) in watermarking algorithm. Unlike classical CS-based watermark generation method, lifting wavelet transformation, partial Hadamard matrix, and ternary watermark sequence have been combined together to carry sufficient watermark information to ensure reconstruction accuracy and robustness. In the procedure of watermark embedding and extraction, watermark is embedded and extracted in CS measurement of remote sensing image. Hence the whole algorithm security is guaranteed by CS measurement matrix either in watermark generation or watermark embedding and extraction. Then, the CS-based watermarking algorithm for remote sensing images is proposed and demonstrated. Compared with other CS-based approaches, the improvements on reconstruction accuracy, security and robustness of the proposed algorithm have been verified by experiments.


Watermarking Compressive sensing Security Robustness Reconstruction accuracy 



  1. 1.
    Al-Mansoori S (2012) An efficient watermarking technique for satellite images using discrete cosine transform. In: Huang B, Plaza AJ (eds) High-performance computing in remote sensing II, vol 8539. Proceedings of SPIE. Spie-Int Soc Optical Engineering, Bellingham.
  2. 2.
    Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202. MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425. MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30. CrossRefGoogle Scholar
  5. 5.
    Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509. MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chang CP, Lee YC, Chang AC, Huang PS, Tu TM (2006) StarMarker—a fast and robust RGB-based saturation watermarking system for pan-sharpened IKONOS and QuickBird imagery. Opt Eng 45(5):056202. CrossRefGoogle Scholar
  7. 7.
    Chen Q, Xie PP, Ma WJ, Wei WY, Ai LH (2010) A digital watermarking algorithm based on characters of the remote-sensing imagery. In: 2010 International Conference on Management and Service Science, 24-26 Aug. 2010. p 1–4.
  8. 8.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306. MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Ender JHG (2010) On compressive sensing applied to radar. Signal Process 90(5):1402–1414. CrossRefzbMATHGoogle Scholar
  10. 10.
    Fang LY, Li ST, Nie Q, Izatt JA, Toth CA, Farsiu S (2012) Sparsity based denoising of spectral domain optical coherence tomography images. Biomed Opt Express 3(5):927–942. CrossRefGoogle Scholar
  11. 11.
    Fang H, Zhou Q, Li K (2013) Robust watermarking scheme for multispectral images using discrete wavelet transform and tucker decomposition. J Comput 11(8):7. Google Scholar
  12. 12.
    Figueiredo MAT, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process 1(4):586–597. CrossRefGoogle Scholar
  13. 13.
    Fu JJ, Wang K, Xu JJ (2016) A copyright protection scheme for multiband digital remote sensing imagery. Acta Electron Sin 3(44):8. Google Scholar
  14. 14.
    Ghaffari A, Babaie-Zadeh M, Jutten C (2009) Sparse decomposition of two dimensional signals. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 19-24 April 2009. p 3157-3160.
  15. 15.
    Hsu PH, Chen CC (2016) A robust digital watermarking algorithm for copyright protection of aerial photogrammetric images. Photogramm Rec 31(153):51–70. CrossRefGoogle Scholar
  16. 16.
    Huang HC, Chang FC, Wu CH, Lai WH (2012) Watermarking for compressive sampling applications. In: 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 18-20 July 2012. p 223-226.
  17. 17.
    Langelaar GC, Setyawan I, Lagendijk RL (2000) Watermarking digital image and video data - a state-of-the-art overview. IEEE Signal Process Mag 17(5):20–46. CrossRefGoogle Scholar
  18. 18.
    Li LL, Sun JG (2012) A watermarking algorithm for remote sensing image based on DFT and watermarking segmentation. In: Zhang CS (ed) Materials Science and Information Technology, Pts 1-8, vol 433-440. Advanced Materials Research. Trans Tech Publications Ltd, Durnten-Zurich, pp 2504.
  19. 19.
    Liu Y, Nie LQ, Han L, Zhang LM, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Ijcai-Int Joint Conf Artif Intell, FreiburgGoogle Scholar
  20. 20.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. Paper presented at the Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, ArizonaGoogle Scholar
  21. 21.
    Liu Y, Nie LQ, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115. CrossRefGoogle Scholar
  22. 22.
    Liu CZ, Chen QC, Liang HB, Li HC (2016) Digital watermarking processing technique based on overcomplete dictionary. Int J Pattern Recogn 30(10).
  23. 23.
    Liu H, Xiao D, Zhang R, Zhang YS, Bai S (2016) Robust and hierarchical watermarking of encrypted images based on compressive sensing. Signal Process Image Commun 45:41–51. CrossRefGoogle Scholar
  24. 24.
    Lu P, Xu ZY, Lu X, Liu XY (2013) Digital image information encryption based on compressive sensing and double random-phase encoding technique. Optik 124(16):2514–2518. CrossRefGoogle Scholar
  25. 25.
    Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195. CrossRefGoogle Scholar
  26. 26.
    Melgani F, Benzid R, De Natale FGB (2007) Near-lossless spread spectrum watermarking for multispectral remote sensing images. J Appl Remote Sens 1:17. Google Scholar
  27. 27.
    Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for overcomplete sparse decomposition based on smoothed l0 norm. IEEE Trans Signal Process 57(1):289–301. MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Rachlin Y, Baron D (2008) The secrecy of compressed sensing measurements. Ann Allerton Conf:813.
  29. 29.
    Ren N, Zhu C, Wang Z (2011) Blind watermarking algorithm based on mapping mechanism for remote sensing image. Acta Geodaetica et Cartographica Sinica 40(5):623–627Google Scholar
  30. 30.
    Serra-Ruiz J, Megias D (2012) Reversible data hiding for tampering detection in remote sensing images using histogram shifting. In: Huang B, Plaza AJ, Thiebaut C (eds) Satellite Data Compression, Communications, and Processing Viii, vol 8514. Proceedings of SPIE. Spie-Int Soc Optical Engineering, Bellingham.
  31. 31.
    Shinoda K, Watanabe A, Hasegawa M, Kato S (2015) Multispectral information hiding in RGB image using bit-plane-based watermarking and its application. Opt Rev 22(3):469–476. CrossRefGoogle Scholar
  32. 32.
    Somani SM, Mohan BK, Choksi DV, Venkatachalam P, Habib T (2016) Robust Watermarking of Satellite Images using Texture-based LSB-DWT Method. Proc Spie 9880.
  33. 33.
    Sui LS, Zhou B, Wang ZM, Tian AL (2017) An optical color image watermarking scheme by using compressive sensing with human visual characteristics in gyrator domain. Opt Lasers Eng 92:85–93. Google Scholar
  34. 34.
    Sweldens W (1998) The lifting scheme: a construction of second generation wavelets. SIAM J Math Anal 29(2):511–546. MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Szekely GJ, Rizzo ML, Bakirov NK (2007) Measuring and testing dependence by correlation of distances. Ann Stat 35(6):2769–2794. MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Thakkar FN, Srivastava VK (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. CrossRefGoogle Scholar
  37. 37.
    Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666. MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Zhang YS, Zhang LY, Zhou JT, Liu LC, Chen F, He X (2016) A review of compressive sensing in information security field. IEEE Access 4:2507–2519. CrossRefGoogle Scholar
  39. 39.
    Zhao H, Cai J, Zhu L (2015) A robust information hiding scheme for protecting digital content in DWT-CS domain. International Journal of Security and Its Applications 9(12):245–254. CrossRefGoogle Scholar
  40. 40.
    Zhou N, Zhang A, Zheng F, Gong L (2014) Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Opt Laser Technol 62:152–160. CrossRefGoogle Scholar
  41. 41.
    Zhu XX, Bamler R (2013) A sparse image fusion algorithm with application to pan-sharpening. IEEE Trans Geosci Remote Sens 51(5):2827–2836. CrossRefGoogle Scholar
  42. 42.
    Zhu P, Jia F, Zhang JL (2013) A copyright protection watermarking algorithm for remote sensing image based on binary image watermark. Optik 124(20):4177–4181. CrossRefGoogle Scholar
  43. 43.
    Zope-Chaudhari S, Venkatachalam P, Buddhiraju KM (2015) Secure dissemination and protection of multispectral images using crypto-watermarking. IEEE J Sel Top Appl Earth Observ Remote Sens 8(11):5388–5394. CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University)Ministry of EducationNanjingChina
  2. 2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)NanjingChina
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

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