Skip to main content

Advertisement

SpringerLink
Log in

Steganography in discrete wavelet transform based on human visual system and cover model

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

Abstract

In this paper we present a model-based image steganography method in discrete wavelet transform (DWT). This method is based on the human visual system model. The proposed steganography method assumes a model for cover image statistics. In this algorithm, the DWT coefficients are used as the carrier of the hidden message. An unpleasant outcome of this algorithm is that its perceptual characteristic is degraded. The perceptual detectability weakness of this approach is improved by introducing another algorithm which is proposed based on the Watson visual system model to prevent visually perceptible changes during embedding. In the first step, the maximum tolerable change in each DWT coefficient is extracted using the human visual model. Then, a model is fitted to the histogram of low-precision coefficients and the message bits are encoded to this model. In the final step, the encrypted message bits are embedded in the coefficients whose maximum possible changes are visually imperceptible. Experimental results illustrate that changes occurred during data embedding by employing the human visual model leads to perceptually undetectable changes. The perceptual detectability is satisfied while the perceptual quality and the security usually increased. The perceptual quality is measured by structural similarity measure, and the security is measured by two well-known steganalysis 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

Similar content being viewed by others

References

  1. Ahmidi N, Neyestanak AL (2008) A human visual model for steganography. Proc IEEE Canadian Conf electrical computer engineering (CCECE), Niagara Falls, ON, USA, pp 1077–1080

  2. Akhbari B, Ghaemmaghami S (2005) Watermarking of still images in wavelet domain based on entropy masking model. Proc IEEE region 10 Int Conf TENCON, Melbourne, Qld, Australia, pp 1–6

  3. Amar M et al (2016) A JND model using a texture-edge selector based on Faber-Schauder wavelet lifting scheme. Proc 7th Int Conf image and signal process, ICISP 2016, pp 328–336

  4. Awrangjeb M, Kankanhalli MS (2003) Lossless watermarking considering the human visual system. Digit watermarking, Seoul, Korea, vol 2939, pp 581–592

  5. Bae SH, Kim M (2016) DCT-QM: a DCT-based quality degradation metric for image quality optimization problems. IEEE Trans Image Process 5(10):4916–4930

    Article  MathSciNet  MATH  Google Scholar 

  6. Böhme R (2010) Advanced statistical Steganalysis. Springer, Berlin

    Book  MATH  Google Scholar 

  7. Bohme R, Westfeld A (2004) Breaking Cauchy model-based JPEG steganography with first order statistics. Proc of ESORICS, vol 3193, pp 125–140

  8. Cachin C (1998) An information-theoretic model for steganography. Proc second Int workshop Inf hiding (IH'98), Portland, Oregon, USA. Springer

  9. Cheddad A et al (2010) Digital image steganography: survey and analysis of current methods. Signal Process 90(3):727–752

    Article  MATH  Google Scholar 

  10. Chen TH, Horng G, Wang SH (2003) A robust wavelet-based watermarking scheme using quantization and human visual system mode. Pakistan J Inf Technol 2(3):213–230

    Google Scholar 

  11. Cover T, Thomas J (1991) Elements of information theory. Wiley, New York

    Book  MATH  Google Scholar 

  12. Cox IJ et al (2008) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann, Burlington

    Google Scholar 

  13. Delaigle JF, Vleeschouwer CD, Macq B (1998) Watermarking algorithm based on a human visual model. Signal Process 66(3):319–335

    Article  MATH  Google Scholar 

  14. Droogenbroeck MV, and Delvaux J (2002) An entropy based technique for information embedding in images. Proc 3rd IEEE Benelux signal process symposium (SPS-2002)

  15. Fakhredanesh M, Rahmati M, Safabakhsh R (2013) Adaptive image steganography using contourlet transform. J Electron Imaging 22(4):043007

    Article  Google Scholar 

  16. Fakhredanesh M, Safabakhsh R, Rahmati M (2014) A model-based image steganography method using Watson's visual model. ETRI J 36(3):479–489

    Article  Google Scholar 

  17. Fridrich J (2004) Feature-based Steganalysis for JPEG images and its implications for future Design of Steganographic Schemes. Proc IH, Toronto, Canada, vol 3200, pp 67–81

  18. Fridrich J, Du R (1999) Secure Steganographic methods for palette images. Proc IH, New York, USA, pp 47–60

  19. Fridrich J, Goljan M (2003) Digital image steganography using stochastic modulation. Sec watermarking multimed contents, vol 5020, pp 191–203

  20. Hu R, Chen F, Yu H (2010) Incorporating Watson's perceptual model into patchwork watermarking for digital images. Proc Int Conf image Vis (ICIP'10), Hong Kong, Sept 26–29, pp 3705–3708

  21. Huang H, Huang S, Chen J, Wang R, Xiong J (2014) An image information hiding algorithm based on grey system theory. Int J Commun Syst 27(10):2426–2442

    Article  Google Scholar 

  22. Jayalakshmi M, Merchant SN, Desai UB (2006) Significant pixel watermarking using human visual system model in wavelet domain. Proc CVGIP, Madurai, India, pp 206–215

  23. Jung YJ, Hahn M, Ro YM (2003) Spatial frequency band division in human visual system based watermarking. Proc IWDW, Seoul, Korea, pp 224–234

  24. Jung SW, Ha LT, Ko SJ (2011) A new histogram-modification-based reversible data hiding algorithm considering the human visual system. Signal Process Lett, IEEE 18(2):95–98

    Article  Google Scholar 

  25. Kim SW, and Suthaharan S (2004) An entropy masking model for multimedia content watermarking. Proc 37th Annu Hawaii Int Conf system Sci

  26. Kim YS, Kwon OH, Park RH (1999) Wavelet based watermarking method for digital images using the human visual system. Proc ISCAS, vol 4, pp 80–83

  27. Kodovsky J, Fridrich J (2009) Calibration revisited. Proc MM&sec, Princeton, NJ, USA, pp 62–74

  28. Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for Steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444

    Article  Google Scholar 

  29. Kwon OH, Kim YS, Park RH (1999) Watermarking for still images using the human visual system in the DCT domain. Proc ISCAS, Orlando, FL, USA, vol 4, pp 76–79

  30. Levicky D, Foris P (2004) Human visual system models in digital image Watermarking. Radioengineering 13:38–43

    Google Scholar 

  31. Li Y et al (2008) An adaptive blind watermarking algorithm based on DCT and modified Watson's visual model. Proc ISECS, Guangzhou, China, pp 904–907

  32. Lou DC, Liu JL, Hu MC (2003) Adaptive digital watermarking using neural network technique. Proc ICCST, pp 325–332

  33. Marvel LM, Boncelet CG, Retter CT (1999) Spread Spectrum image steganography. IEEE Trans Image Process 8(8):1075–1083

    Article  Google Scholar 

  34. Nguyen PB, Beghdadi A, Luong M (2013) Perceptual watermarking using a new just-noticeable-difference model. Signal process: image. Communication 28(10):1506–1525

    Google Scholar 

  35. Niu B (2017) An improvement image subjective quality evaluation model based on just noticeable difference. Proc 12th Int Conf Intell Inf hiding multimed signal Vis, Kaohsiung, Taiwan, vol 2, pp 93–100

  36. Oueslati S, Cherif A, Solaiman B (2010) A fuzzy watermarking approach based on the human visual system. Int J Image Process 4(3):218–231

    Google Scholar 

  37. Pan F et al (2011) Steganography based on minimizing embedding impact function and HVS. Proc ICECC, Zhejiang, China, pp 490–493

  38. Pevny T, Fridrich J (2007) Merging Markov and DCT features for multi-class JPEG Steganalysis. Proc SPIE, San Jose, CA, USA, vol 6505, p 650503–650503–13

  39. Podilchuk CI, Zeng W (1997) Perceptual watermarking of still images. Proc IEEE multimed signal process, Princeton, NJ, USA, pp 363–368

  40. Podilchuk CI, Zeng W (1998) Image-adaptive watermarking using visual models. IEEE J Selected Areas Communications 16(4):525–539

    Article  Google Scholar 

  41. Porter J, Rajan P (2006) Image adaptive watermarking techniques using models of the human visual system. Proc SSST, Cookeville, TN, USA, pp 354–357

  42. Provos N (2001) Defending against statistical steganalysis. Proc USENIX Secur Symp, Washington, WA, USA, pp 323–335

  43. Qazanfari K, Safabakhsh R (2011) A new adaptive method for hiding data in images. Proc 6th Iranian Conf machine vision image process, Tehran, Iran

  44. Qazanfari K, Safabakhsh R (2012) Adaptive method for hiding data in images. J Electron Imaging 21(1):013022

    Article  Google Scholar 

  45. Qazanfari K, Safabakhsh R (2013) High-capacity method for hiding data in the discrete cosine transform domain. J Electron Imaging 22(4):043009

    Article  Google Scholar 

  46. Qazanfari K, Safabakhsh R (2014) A new steganography method which preserves histogram: generalization of LSB++. Inf Sci 277:90–101

    Article  MathSciNet  Google Scholar 

  47. Qin C, Chang CC, Lin CC (2015) An adaptive reversible steganographic scheme based on the just noticeable distortion. Multimed Tools Appl 74(6):1983–1995

    Article  Google Scholar 

  48. Roy A, Maiti AK, Ghosh K (2015) A perception based color image adaptive watermarking scheme in YCbCr space. 2015 2nd Int Conf Signal Process Integr Netw (SPIN), Noida, pp 537–543

  49. Safabakhsh R, Zaboli S, and Tabibiazar A (2004) Digital watermarking on still images using wavelet transform. Proc IEEE Int Conf Inf technology: coding and computing, ITCC

  50. Sallee P (2003) Model-based steganography. Digit watermarking, Seoul, Korea, vol 2939, pp 154–167

  51. Sallee P (2005) Model-based methods for steganography and steganalysis. Int J Image and graphics 5(01):167–189

    Article  Google Scholar 

  52. Shu Z et al (2008) Watermarking algorithm based on Contourlet transform and human visual model. Proc ICESS, Sichuan, China, pp 348–352

  53. Shu Z et al (2009) Watermarking algorithm based on Curvelet transform and human visual model. Proc ISECS, Nanchang, China, vol 1, pp 208–212

  54. Tang W, Wan W, Liu J, Sun J (2015) Improved spread transform dither modulation using luminance-based JND model. Proc 8th Int Conf image and graphics, ICIG 2015 II, pp 430–437

  55. Tsai MJ, Liu J, Yin JS, Yuadi I (2014) A visible wavelet watermarking technique based on exploiting the contrast sensitivity function and noise reduction of human vision system. Multimed Tools Appl 72(2):1311–1340

    Article  Google Scholar 

  56. Ullerich C, Westfeld A (2007) Weaknesses of MB2. 6th Int workshop digital watermarking (IWDW 2007). Springer, Berlin, pp 127–142

  57. Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  58. Washington image database. www.cswashington.edu/research/imagedatabase/groundtruth/. Accessed 2012

  59. Watson AB (1993) DCT quantization matrices visually optimized for individual images. Proc SPIE, San Jose, CA, USA, vol 1913, pp 202–216

  60. Watson AB et al (1997) Visibility of wavelet quantization noise. IEEE Trans Image Process 6(8):1164–1175

    Article  Google Scholar 

  61. Watson AB, R Borthwick, and M Taylor (1997) Image quality and entropy masking. Proc SPIE 3016, human Vis electronic imaging II, San Jose, CA, USA

  62. Westfeld A (2001) F5-a Steganographic algorithm. Proc IH, Pittsburgh, PA, USA, pp 289–302

  63. Xie G, Swamy M, Ahmad MO (2006) Perceptual-shaping comparison of DWT-based pixel-wise masking model with DCT-based Watson model. Proc ICIP, Atlanta, GA, pp 1381–1384

  64. Zhang Y (2009) Blind watermark algorithm based on HVS, and RBF neural network in DWT domain. W trans on Comput, Stevens Point, WI, USA vol 8, no 1, pp 174–183

  65. Zhang X, Wang S (2005) Steganography using Multiple-Base notational system and human vision sensitivity. Signal Process Lett, IEEE 12(1):67–70

    Article  MathSciNet  Google Scholar 

  66. Zhang Y, Yang C, Zhang Q (2014) Primal sketch based visual entropy model for digital watermarking. 2014 10th Int Conf natural computation (ICNC), Xiamen, pp 958–963

  67. Zhu G, Sang N (2008) An adaptive quantitative information hiding algorithm based on DCT domain of new visual model. Proc ISISE, Shanghai, China, vol 1, pp 546–550

Download references

Author information

Authors and Affiliations

  1. Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran

    Mohammad Fakhredanesh, Mohammad Rahmati & Reza Safabakhsh

Authors
  1. Mohammad Fakhredanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Rahmati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Reza Safabakhsh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Fakhredanesh.

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

Fakhredanesh, M., Rahmati, M. & Safabakhsh, R. Steganography in discrete wavelet transform based on human visual system and cover model. Multimed Tools Appl 78, 18475–18502 (2019). https://doi.org/10.1007/s11042-019-7238-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7238-8

Keywords

Search

Navigation