Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8115–8138 | Cite as

Curvelet transform and cover selection for secure steganography

  • Mansi S. Subhedar
  • Vijay H. Mankar


In this paper, we present curvelet transform (CT) based image steganography that embeds scrambled secret image in appropriately selected cover image. Curvelet transform offers optimal nonadaptive sparse representation of objects with edges and possesses high directional sensitivity and anisotropy. Cover image is decomposed using curvelet transform and adaptive block based embedding is carried out only in non-uniform regions of high frequency curvelet coefficients. In addition, this work also demonstrates a new cover selection method to choose suitable cover from image database. Spatial information based image complexity is modelled using fuzzy logic to identify set of images that yields least detectable stego image. From this set of ranked images, best cover can be chosen for carrying secret information depending on amount of information to be embedded. Cover selection offers reduced risk of detectability and ensures security. It is evident from experimental results that proposed method outperforms conventional methods in terms of imperceptibility, robustness and security.


Image steganography Image complexity Curvelet transform Image quality Steganalysis 


  1. 1.
    Al Dmour H, Al Ani A (2016) A steganography embedding method based on edge identification and XOR coding. Expert Syst Appl 46:293–306CrossRefGoogle Scholar
  2. 2.
    ANSI T1.801.03 (1996) Digital transport of one way video signals parameters for objective performance assessment, American National Standards InstituteGoogle Scholar
  3. 3.
    Boubchir L, Fadili J (2005) Multivariate statistical modelling of images with the curvelet transform. In: Proceedings of the 8th international conference on signal processing, pattern recognition and applications, pp 747–750Google Scholar
  4. 4.
    Candes EJ, Demanet L, Donoho DL, Ying L (2005) Fast discrete curvelet transforms. Tech Report, Calif. Inst. of Tech.Google Scholar
  5. 5.
    Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: survey and analyses of current methods. Signal Process 90(3):727–752CrossRefzbMATHGoogle Scholar
  6. 6.
    Cho S, Ho Cha B, Wang J, Jay Kuo CC (2010) Block-Based Image Steganalysis: Algorithm and performance evaluation. In: IEEE International symposium on circuits and systems (ISCAS), ParisGoogle Scholar
  7. 7.
    Donoho DL (1995) Denoising by soft thresholding. IEEE Trans Inf Theory 1 (3):613–627. doi: 10.1109/18.382009 CrossRefzbMATHGoogle Scholar
  8. 8.
    Donoho DL, Duncan MR (2000) Digital curvelet transform: strategy, implementation and experiments. Proc SPIE 4056:12–29CrossRefGoogle Scholar
  9. 9.
    Farid H (2002) Detecting hidden messages using higher-order statistical models. In: International conference on image processing, RochesterGoogle Scholar
  10. 10.
    Fridrich J (2004) Feature based steganalysis for JPEG images and its implications for future design of steganographic schemes. In:. In: Proceedings of the 6th Info. Hiding Workshop, TorontoGoogle Scholar
  11. 11.
    Fridrich J, Pevny T, Kodovsky J (2007) Statistically Undetectable, JPEG Steganography: Dead Ends, Challenges, and Opportunities, MM & Sec’07. ACM, DallasCrossRefGoogle Scholar
  12. 12.
    Gulve AK, Joshi MS (2015) An image steganography method hiding secret data into coefficients of integer wavelet transform using pixel value differencing approach. Hindawi Mathematical Problems in Engineering 2015, Article ID 684824Google Scholar
  13. 13.
    Honghai Yu, Winkler S (2013) Image complexity and spatial information. In: 5th international workshop on quality of multimedia experience (QoMEX), pp 12–17. doi: 10.1109/QoMEX.2013.6603194
  14. 14.
  15. 15.
    Kanan HR, Nazeri B (2014) A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Systems with Applications 41(14):6123–6130CrossRefGoogle Scholar
  16. 16.
    Kharrazi M, Sencar HT, Memon N (2006) Cover selection for steganographic embedding. In: International conference on image processing, pp 117–120Google Scholar
  17. 17.
    Khosravi MJ, Naghsh-Nilchi AR (2014) A novel joint secret image sharing and robust steganography method using wavelet. Multimedia Systems 20:215–226CrossRefGoogle Scholar
  18. 18.
    Li B, He J, Huang J, Shi YQ (2011) A survey on image steganography and steganalysis. International journal of information hiding and multimedia signal processing 2(2):142–172Google Scholar
  19. 19.
    Lyu S, Farid H (2003) Detecting hidden messages using higher order statistics and support vector machines. IH2002, LNCS 2578:340–354zbMATHGoogle Scholar
  20. 20.
    Mostafa H, Ali AF, EI Taweal G (2015) Hybrid curvelet transform and least significant bit for image steganography. In: IEEE 7th international conference on intelligent computing and information systems, pp 300–305. doi: 10.1109/IntelCIS.2015.7397238
  21. 21.
    Muhammad N, Bibi N, Mahmood Z, Kim DG (2015) Blind data hiding technique using the Fresnelet transform SpringerPlus. doi: 10.1186/s40064-015-1534-1
  22. 22.
    Nazari S (2013) Cover selection steganography via run length matrix and human visual system. J Inform Syst Telecommu 1(2):131–138Google Scholar
  23. 23.
    Rabie T, Kamel I (2016) High capacity steganography: a global adaptive region discrete cosine transform approach. Multimedia Tools and ApplicationsGoogle Scholar
  24. 24.
    Sajasi S, Moghadam AME (2015) An adaptive image steganographic scheme based on noise visibility function and an optimal chaotic based encryption method. Appl Soft Comput 30:375–389CrossRefGoogle Scholar
  25. 25.
    Sajedi H, Jamzad M (2008) Adaptive steganography method based on contourlet transform. In: 9th international conference on signal processing, pp 745–748. doi: 10.1109/ICOSP.2008.4697237
  26. 26.
    Sajedi H, Jamzad M (2008) Cover selection steganography method based on similarity of image blocks. In: IEEE 8th international conference on computer and information technology, pp 379–384. doi: 10.1109/CIT.2008.Workshops.34
  27. 27.
    Sajedi H, Jamzad M (2010) CBS: Contourlet based steganalysis method. J Signal Proc Syst 61:367–373CrossRefGoogle Scholar
  28. 28.
    Sajedi H, Jamzad M (2010) Using contourlet transform and cover selection for secure steganography. Int J Inf Secur 9:337–352. doi: 10.1007/s10207-010-0112-3 CrossRefGoogle Scholar
  29. 29.
    Schaefer G, Stich M (2004) Ucid - an uncompressed colour image database. Storage and Retrieval Methods and Applications for Multimedia 5307:472–80Google Scholar
  30. 30.
    Shirafkan MH, Akhtarkavan E, Vahidi J (2015) A image steganography scheme based on discrete wavelet transform using lattice vector quantization and reed Solomon encoding. In: 2nd International conference on knowledge based engineering and innovationGoogle Scholar
  31. 31.
    Starck JL, Candes E, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Proc 11(6):670–684MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Subhedar MS, Mankar V (2014) Current status and key issues in image steganography: A survey. Comput Sci Rev 13(14):95–113CrossRefzbMATHGoogle Scholar
  33. 33.
    Subhedar MS, Mankar VH (2016) Image steganography using redundant discrete wavelet transform and QR factorization. Comput Elect Eng. doi: 10.1016/j.compeleceng.2016.04.017
  34. 34.
    Sun Y, Liu F (2010) Selecting cover for image steganography by correlation coefficient. In: 2nd International Workshop on Education Technology and Computer Science (ETCS), vol 2, pp 159–162Google Scholar
  35. 35.
    Thabit R, Khoo BE (2015) A new robust lossless data hiding scheme and it’s application to color medical images. Digital Signal Processing 38:77–94CrossRefGoogle Scholar
  36. 36.
  37. 37.
    Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Processing Letters 9(3):81–84CrossRefGoogle Scholar
  38. 38.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612CrossRefGoogle Scholar
  39. 39.
    Xiao M, He Z (2015) High capacity image steganography method based on framelet and compressive sensing.. In: Proceedings of the SPIE, Multispectral Image Acquisition, Processing and Analysis 9811:98110YGoogle Scholar
  40. 40.
    Yu C Steganography of digital watermark by Arnold scrambling transform with blind source separation morphological component analysis. Multimed Tools Appl. doi: 10.1007/s11042-015-3205-1
  41. 41.
    Zadeh LA (1995) Fuzzy sets. Information and Control 8(3):338–353CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics & Telecommunication, BD College of EngineeringWardhaIndia
  2. 2.Department of Electronics & Telecommunication, Government PolytechnicNagpurIndia

Personalised recommendations