Skip to main content

Advertisement

Springer Nature Link
Log in

Edge based image steganography with variable threshold

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

Abstract

With the rapid escalation of the usage of sensitive data exchange through internet, information security has become a vulnerable realm of concern. Numerous security policies are incorporated, steganography being one of them. In this paper, a new image steganography technique facilitating embedding and extraction mechanisms have been proposed. The classic technique of edge detection is employed in which the threshold is made to vary for every new input test case. Relying on the threshold generation methodology, each edge image is generated in accordance with a distinct key threshold value. This in turn directs the insertion and extraction strategies. By using different performance metrics, like Payload, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, Kullback-Leibler Divergence, Cross-correlation, etc., this procedure substantiates better results. It has quite a remarkable embedding capacity (i.e. Payload). Also the values of Peak Signal to Noise Ratio and Structural Similarity Index indicate that the imperceptibility of the stego-image is well-maintained. The statistical results obtained reassure the undetectability of the hidden image. This approach may be used to deliver a status of security to a system which communicates secure data files through any public surveillance medium.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Abdallah EE, Ben Hamza A, Bhattacharya P (2007) MPEG Video Watermarking Using Tensor Singular Value Decomposition, International Conference Image Analysis and Recognition, ICIAR 2007: Image Analysis and Recognition, p 772–783

  2. Abdallah EE, Ben Hamza A, Bhattacharya P (2010) Video watermarking using wavelet transform and tensor algebra. SIViP 4(2):233–245

    Article  MATH  Google Scholar 

  3. Ahmad T, Abdullah M (2009) A novel steganographic method for gray-level images. World Acad Sci Eng Technol 3(3):3

    Google Scholar 

  4. Almohammad A, Ghinea G (2010) Stego-image quality and the reliability of PSNR, Image Processing Theory, Tools and Applications, IEEE

  5. Altaay AAJ, Sahib SB., Zamani M (2012) An Introduction to Image Steganography Techniques, IEEE International Conference on Advanced Computer Science Applications and Technologies (ACSAT)

  6. Banerjee I, Indu P, Singh A et al (2015) Robust watermarking using four bit per pixel technique. Int J Electron Secur Digit Forensics 7(4)

  7. Bansal R, Gupta S, Sharma G (2017) An innovative image encryption scheme based on chaotic map and Vigenère scheme. Multimed Tools Appl 76:16529–16562

    Article  Google Scholar 

  8. Bhattacharyya D, Kim T (2011) Image data hiding technique using discrete Fourier transformation. In: Kim T, Adeli H, Robles RJ, Balitanas M (eds) Ubiquitous computing and multimedia applications. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, p 151

    Google Scholar 

  9. Bhattacharyya S, Sanyal G (2012) Study and analysis of quality of service in different image based steganography using pixel mapping method. International Journal of Applied Information Systems 2(7):42–57

    Google Scholar 

  10. Chang CC, Chen TS, Chung LZ (2002) A steganographic method based upon JPEG and quantization table modification. Inf Sci 141:123–138

    Article  MATH  Google Scholar 

  11. Dukkipati A (2012) On maximum entropy and minimum KL-divergence optimization by Gröbner basis methods. Appl Math Comput 218(23):11674–11687

    MathSciNet  MATH  Google Scholar 

  12. Duncan K, Sarkar S (2012) Relational entropy-based saliency detection in images and videos. 19th IEEE International Conference on Image Processing, p 1093–1096

  13. El-Alfy E, Al-Sadi A (2011) A comparative study of PVD-based schemes for data hiding in digital images. In: Siegel HJ, El-Kadi A (eds) AICCSA (IEEE Computer Society), p 144–149

  14. Elsheh E, Ben Hamza A (2011) Secret sharing approaches for 3D object encryption. Expert Syst Appl 38(11):13906–13911

    Google Scholar 

  15. Feng J, Zhang J, Zhu X et al (2017) A novel chaos optimization algorithm. Multimed Tools Appl 76:17405–17436

    Article  Google Scholar 

  16. Ferzli R, Girija L, Ali W (2010) Efficient implementation of kurtosis based no reference image sharpness metric. In: Astola J, Egiazarian KO (eds) Image processing: algorithms and systems, (SPIE), p 75320

  17. Gurav M, MukeshTiwari P, Singh P (2015) High secured image by LSB steganography technique using matlab. International Journal on Recent and Innovation Trends in Computing and Communication 3(4):1836–1840

    Article  Google Scholar 

  18. Huang P, Chang K, Chang C et al (2008) A novel image steganography method using tri-way pixel value differencing. J Multimed 3

  19. Kahn J, Kalai G (2007) Thresholds and expectation thresholds. Comb Probab Comput 16(3):495–502

    Article  MathSciNet  MATH  Google Scholar 

  20. Kanan H, Nazeri B (2014) A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl 41(14):6123–6130

    Article  Google Scholar 

  21. Koo H, Cho N (2013) Skew estimation of natural images based on a salient line detector. J Electron Imaging 22(1):013020

    Article  Google Scholar 

  22. Kumar V, Gupta P (2012) Importance of statistical measures in digital image processing. International Journal of Emerging Technology and Advanced Engineering 2(8):56–62

    Google Scholar 

  23. Lan T, Mansour M, Tewfik A (2000) Robust high capacity data embedding. In: ICIP, p 581–584

  24. Li Q, Fang YM, Xu JT (2016) A novel spatial pooling strategy for image quality assessment. J Comput Sci Technol 31(2):225–234

    Article  Google Scholar 

  25. Liu L, Miao S (2017) An image encryption algorithm based on baker map with varying parameter. Multimed Tools Appl 76:16511–16527

    Article  Google Scholar 

  26. Luo W, Huang F, Huang J (2010) Edge adaptive image steganography based on LSB matching revisited. IEEE Trans Inf Forensics Secur 5(2):201–214

    Article  Google Scholar 

  27. Mohapatra S, Kumar P, Majhi B (2012) Adaptive threshold selection for impulsive noise detection in images using coefficient of variance. Neural Comput & Applic 21(2):281–288

    Article  Google Scholar 

  28. Muhammad K, Ahmad J, Rho S et al (2017) Image steganography for authenticity of visual contents in social networks. Multimed Tools Appl 76:18985–19004

    Article  Google Scholar 

  29. Mukherjee S, Sanyal G (2017) Enhanced position power first mapping (PPFM) based image steganography. Int J Comput Appl 39(2). Taylor and Francis

  30. Mukherjee S, Ash S, Sanyal G (2015) A novel image steganographic methodology by Power Modulus Scrambling with logistic mapping, TENCON 2015, 2015 IEEE Region 10 Conference

  31. Mukherjee S, Ash S, Sanyal G (2015) A novel image steganographic approach by Pixel Position Modulus Method (PPMM), Computing for Sustainable Global Development (INDIACom), IEEE Conference Publications, 201

  32. Potdar V, Chang E (2004) Gray level modification steganography for secret communication. IEEE International Conference on Industrial Informatics, Berlin, Germany, p 355–368

  33. Safarpour M, Charmi M (2016) Capacity enlargement of the PVD steganography method using the GLM technique, CoRRabs, 1601.00299

  34. Sajedi H (2016) Steganalysis based on steganography pattern discovery. Journal of Information Security and Applications 30:3–14

    Article  Google Scholar 

  35. Sheisi H, Mesgarian J, Rahmani M (2012) Steganography: Dct Coefficient Replacement Method and Compare with JSteg Algorithm. International Journal of Computer and Electrical Engineering 4(4)

  36. Solemani S, Taherinia A (2017) High capacity image steganography on sparse message of scanned document image (SMSDI). Multimed Tools Appl 76:20847–20867

    Article  Google Scholar 

  37. Subhedar M, Mankar V (2014) Current status and key issues in image steganography: a survey. Computer Science Review 13-14:95–113

    Article  MATH  Google Scholar 

  38. Sun S (2016) A novel edge based image steganography with 2k correction and Huffman encoding. Inf Process Lett 116(2):93–99

    Article  MATH  Google Scholar 

  39. Tao H, Chongmin L, Zain JM, Abdalla AN (2014) Robust Image Watermarking Theories and Techniques: A Review. J Appl Res Technol 12(1):122–138

    Article  Google Scholar 

  40. Tiwari N, Sandilya M (2014) Spatial domain image steganography based on security and randomization. Int J Adv Comput Sci Appl 5(1)

  41. Wakure MA, Holambe AN (2015) A discrete wavelet transform: a steganographic method for transmitting images. Int J Comput Appl 129(5)

  42. Yang C, Weng C, Wang S et al (2008) Adaptive data hiding in edge areas of images with spatial LSB domain systems. IEEE Trans Inf Forensics Secur 3(3):488–497

    Article  Google Scholar 

  43. Yang X et al (2016) Image quality assessment via spatial structural analysis. Comput Electr Eng

  44. Yevseyeva I, Fernandes V, Moorsel A et al (2016) Two-stage security controls selection. International Conference on Enterprise Information Systems/ International Conference on Project Management/ International Conference on Health and Social Care Information Systems and Technologies, 100, p 971–978

Download references

Author information

Authors and Affiliations

  1. CSE, National Institute of Technology, Durgapur, India

    Srilekha Mukherjee & Goutam Sanyal

Authors
  1. Srilekha Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Goutam Sanyal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srilekha Mukherjee.

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

Mukherjee, S., Sanyal, G. Edge based image steganography with variable threshold. Multimed Tools Appl 78, 16363–16388 (2019). https://doi.org/10.1007/s11042-018-6975-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6975-4

Keywords