Advertisement

Fuzzy edge detection based steganography using modified Gaussian distribution

  • S. Dhargupta
  • A. Chakraborty
  • S. K. Ghosal
  • S. Saha
  • R. SarkarEmail author
Article
  • 65 Downloads

Abstract

This paper proposes a fuzzy edge detection based steganography approach to effectively hide data within images. Instead of applying conventional edge detection algorithms, the method uses a fuzzy edge detection approach in order to estimate more number of pixels where the data can be hidden. At the outset, the cover image is masked and the fuzzy edge detection is performed on the masked image thus retaining edge information. The number of bits to be embedded in a particular pixel is dependent on whether the pixel is an edge pixel, where more bits are embedded. In case the pixel is not an edge pixel and also not a background pixel then the amount of data that is to be embedded depends on the Euclidean distance of the respective pixel from the nearest edge pixel and is determined by the Gaussian function. Experimental results ensure that the scheme offers variable payload with acceptable quality distortion in the stego-image.

Keywords

Steganography Edge detection Fuzzy approach Gaussian function Payload Cover image 

Notes

References

  1. 1.
    Alshennawy AA, Aly AA (2009) Edge detection in digital images using fuzzy logic technique. Eng Technol 51:185–193Google Scholar
  2. 2.
    Atawneh S, Almomani A, Sumari P (2013) Steganography in digital images: common approaches and tools. IETE Tech Rev 30(4):344CrossRefGoogle Scholar
  3. 3.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  4. 4.
    Chang CC, Hsiaob JY, Chan CS (2003) Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy. Pattern Recogn 36(7):1583–1595CrossRefGoogle Scholar
  5. 5.
    Chen W-J, Chang C-C, Le THN (2010) High payload steganography mechanism using hybrid edge detector. Expert Syst Appl 37(4):3292–3301CrossRefGoogle Scholar
  6. 6.
    Dube RR, Lalkot LA (2016) Improved edge based steganography scheme for GrayScale images in spatial domain. Int J Sci Res 5(6):1976–1978Google Scholar
  7. 7.
    Gupta S, Mazumdar SG (2013) Sobel edge detection algorithm. Int J Comput Sci Manag Res 2(2):1578–1583Google Scholar
  8. 8.
    Gupta GS, Sharma AK, Das BPK, Madhavan CEV (2002) FIAT - fax image analysis tool for steganography and steganalysis. IETE Tech Rev 19(4):221–224CrossRefGoogle Scholar
  9. 9.
    Hussain M, Wahab AWA, Javed N, Jung KH (2018) Recursive information hiding scheme through LSB, PVD shift, and MPE. IETE Tech Rev (Institution Electron Telecommun Eng India) 35(1):53–63Google Scholar
  10. 10.
    Ingemar JC, Miller ML, Jeffrey AB, Fridrich J, Kalker T (2008) Digital watermarking and steganographyGoogle Scholar
  11. 11.
    Islam S, Gupta P (2014) Robust edge based image steganography through pixel intensity adjustment. Proceedings - 16th IEEE international conference on high performance computing and communications, HPCC 2014, 11th IEEE international conference on embedded software and systems, ICESS 2014 and 6th international symposium on cyberspace safety and security: 771–777Google Scholar
  12. 12.
    Jung K-H, Yoo K-Y (2015) High-capacity index based data hiding method. Multimed Tools Appl 74(6):2179–2193CrossRefGoogle Scholar
  13. 13.
    Lan X, Ma AJ, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. 2014 IEEE Conf Comput Vision Pattern Recogn: 1194–1201Google Scholar
  14. 14.
    Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-Cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lan X, Zhang S, Yuen PC, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lan X, Ye M, Zhang S, Zhou H, Yuen PC (2018) Modality-correlation-aware sparse representation for RGB-infrared object tracking. Pattern Recogn LettGoogle Scholar
  17. 17.
    Lee C-F, Chang C-C, Tsou P-L (2010) Data hiding scheme with high embedding capacity and good visual quality based on edge detection. Proceedings - 4th international conference on genetic and evolutionary computing, ICGEC 2010Google Scholar
  18. 18.
    Leng H-S, Tseng H-W (2014) High-payload block-based data hiding scheme using hybrid edge detector with minimal distortion. IET Image Process 8(11):647–654CrossRefGoogle Scholar
  19. 19.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2016) Action2Activity: recognizing complex activities from sensor data. CoRR, vol abs/16110Google Scholar
  20. 20.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  21. 21.
    Ratan R, Madhavan CEV (2002) Steganography based information security. IETE Tech Rev 19(4):213–219CrossRefGoogle Scholar
  22. 22.
    Suneetha D, Kumar DRK (2017) Data hiding using Fibonacci EDGE based steganography for cloud data. Int J Appl Eng Res 12(16):5565–5569Google Scholar
  23. 23.
    Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recogn 34(3):671–683CrossRefGoogle Scholar
  24. 24.
    Wang Z, Yin Z, Zhang X (2018) Distortion function for JPEG steganography based on image texture and correlation in DCT domain. IETE Tech Rev 35(4):351–358CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science & TechnologyNalhati Government PolytechnicBirbhumIndia

Personalised recommendations