SpringerLink
Log in

An interval type-2 fuzzy edge detection and matrix coding approach for color image adaptive steganography

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

Abstract

This paper proposes an adaptive color image steganography technique based on edge detection and matrix coding, by comprehensively considering the human eye sensitivity to edges and the RGB color components. First, non-edge and edge points are detected by computing interval type-2 fuzzy similarity on cover image after noise estimation and filtering. Then, a genetic algorithm is used to optimize the embedding bits (edge and non-edge in RGB channels) according to the capacity of the secret message. Next, a chaotic method and random sequence scrambling are used to prevent the secret message from attacks. Finally, three matrix coding schemes with different embedding efficiency (Least significant bit, Hamming code, and XOR coding) are used to embed the encrypted confidential information. The proposed method hides a large amount of data with good quality of stego image from the human visual system (HVS) and guarantees the confidentiality of communication. Experiments showed that it outperformed several state-of-the-art approaches in terms of payload, mean square error, peak signal-to-noise ratio, and structural similarity. The robustness of the method is also tested by RS steganalysis and pixel difference histogram (PDH) analysis.

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
EUR 32.99 /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 includes VAT (United Kingdom)

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. Bandyopadhyay D, Dasgupta K, Mandal JK, Dutta P, Snášel V (2014) A framework of secured and bio-inspired image steganography using chaotic encryption with genetic algorithm optimization (CEGAO). Springer, Berlin

    Book  Google Scholar 

  2. Bassil Y (2012) Image steganography based on a parameterized canny edge detection algorithm. Int J Comput Appl 60:35–40

    Google Scholar 

  3. Chan CK, Cheng LM (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37:469–474

    Article  MATH  Google Scholar 

  4. Cheddad A, Condell J, Curran K, Kevitt PM (2010) Digital image steganography: survey and analysis of current methods. Signal Process 90:727–752

    Article  MATH  Google Scholar 

  5. Chen WY (2008) Color image steganography scheme using DFT, SPIHT codec, and modified differential phase-shift keying techniques. Appl Math Comput 196:40–54

    MathSciNet  MATH  Google Scholar 

  6. Chen WJ, Chang CC, Le THN (2010) High payload steganography mechanism using hybrid edge detector. Expert Syst Appl 37:3292–3301

  7. Chen PY, Lin HJ (2006) A dwt based approach for image steganography. Int J Appl Sci Eng 4:275–290

    Google Scholar 

  8. Cox IJ, Miller ML, Bloom JA, Fridrich J, Kalker T (2007) Digital watermarking and steganography. Morgan Kaufmann, San Francisco

    Google Scholar 

  9. Crandall R (1998) Some notes on steganography. Posted on steganography mailing list 1998:1–6

    Google Scholar 

  10. Dadgostar H, Afsari F (2016) Image steganography based on interval-valued intuitionistic fuzzy edge detection and modified LSB. J Inform Secur Appl 30:94–104

    Google Scholar 

  11. Eng HL, Ma KK (2000) Noise adaptive soft-switching median filter for image denoising. In: 2000 IEEE international conference on acoustics, speech, and signal processing. IEEE, Istanbul, pp 2175–2178

  12. Fridrich J, Goljan M, Du R (2001) Detecting LSB steganography in color and gray-scale images. IEEE Tranactionson Multimedia 8:22–28

    Article  Google Scholar 

  13. Gaurav K, Ghanekar U (2018) Image steganography based on canny edge detection, dilation operator and hybrid coding. J Inform Secur Appl 41:41–51

    Google Scholar 

  14. Grover N, Mohapatra AK (2013) Digital image authentication model based on edge adaptive steganography. In: 2013 2Nd international conference on advanced computing, networking and security. IEEE, Mangalore, pp 238–242

  15. Guan ZH, Huang F, Guan W (2005) Chaos-based image encryption algorithm. Phys Lett A 346:153–157

    Article  MATH  Google Scholar 

  16. Hayat AD, Ahmed AA (2016) A steganography embedding method based on edge identification and XOR coding. Expert Syst Appl 46:293–306

    Article  Google Scholar 

  17. Hong W (2012) Adaptive reversible data hiding method based on error energy control and histogram shifting. Opt Commun 285:101–108

    Article  Google Scholar 

  18. Hong W, Chen TS (2012) A novel data embedding method using adaptive pixel pair matching. IEEE Trans Inform Forens Secur 7:176–184

    Article  Google Scholar 

  19. Hussain M, Wahid A, Idris Y, Ho A, Jung KH (2018) Image steganography in spatial domain: a survey. Signal Processing Image Communication 65:46–66

    Article  Google Scholar 

  20. Ioannidou A, Halkidis ST, Stephanides G (2012) A novel technique for image steganography based on a high payload method and edge detection. Expert Syst Appl 39:11517–11524

    Article  Google Scholar 

  21. Kadhim IJ, Premaratne P, Vial PJ, Halloran B (2019) Comprehensive survey of image steganography:techniques, evaluations, and trends in future research. Neurocomputing 335:299–326

    Article  Google Scholar 

  22. Kaur M, Juneja M (2017) Adaptive block based steganographic model with dynamic block estimation with fuzzy rules. Springer, Singapore

    Book  Google Scholar 

  23. Kawaguchi E, Eason RO (1999) Principles and applications of BPCS steganography. In: Multimedia systems and applications. SPIE, Boston, pp 464–473

  24. Lee YP, Lee JC, Chen WK, Chang KC, Su IJ, Chang CP (2012) High-payload image hiding with quality recovery using tri-way pixel-value differencing. Inf Sci 191:214–225

    Article  Google Scholar 

  25. Li X, Li J, Li B, Yang B (2013a) High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion. Signal Process 93:198–205

    Article  Google Scholar 

  26. Li J, Li X, Yang B (2013b) Reversible data hiding scheme for color image based on prediction-error expansion and cross-channel correlation. Signal Process 93:2748–2758

    Article  Google Scholar 

  27. Li Q, Wang XY, Wang XY, Ma B, Wang CP, Shi YQ (2021) An encrypted coverless information hiding method based on generative models. Inf Sci 553:19–30

    Article  MathSciNet  MATH  Google Scholar 

  28. Lim Jae S (1990) Two-dimensional signal and image processing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  29. Liu X, Tanaka M, Okutomi M (2012) Noise level estimation using weak textured patches of a single noisy image. In: 2012 19Th IEEE international conference on image processing. IEEE, Orlando, pp 665–668

  30. Liu HJ, Wang XY (2010) Color image encryption based on one-time keys and robust chaotic maps. Comput Math Appl 59:3320–3327

    Article  MathSciNet  MATH  Google Scholar 

  31. Liu HJ, Wang XY (2011) Color image encryption using spatial bit-level permutation and high-dimension chaotic system. Opt Commun 284:3895–3903

    Article  Google Scholar 

  32. Liu HJ, Wang XY, kadir A (2012) Image encryption using DNA complementary rule and chaotic maps. Appl Soft Comput 12:1457–1466

    Article  Google Scholar 

  33. Mendel JM (2007) Type-2 fuzzy sets and systems: an overview. IEEE Comput Intell Mag 2:20–29

    Google Scholar 

  34. Modi MR, Islam S, Gupta P (2013) Edge based steganography on colored images. In: International conference on intelligent computing. Springer, Berlin, pp 593–600

  35. Muhammad K, Ahmad J, Rehman NU, Jan Z, Sajjad M (2017) CISSKA-LSB: Color image steganography using stego key-directed adaptive LSB substitution method. Multimed Tools Appl 76:8597–8626

    Article  Google Scholar 

  36. Nameer NEE (2007) Hiding a large amount of data with high security using steganography algorithm. J Comput Sci 3:223–232

    Article  Google Scholar 

  37. Nameer NEE, Mofleh AD (2015) A novel algorithm for colour image steganography using a new intelligent technique based on three phases. Appl Soft Comput 37:830–846

    Article  Google Scholar 

  38. Noda H, Niimi M, Kawaguchi E (2006) High-performance JPEG steganography using quantization index modulation in DCT domain. Pattern Recogn Lett 27:455–461

    Article  Google Scholar 

  39. Ou B, Li X, Zhao Y, Ni R (2015) Efficient color image reversible data hiding based on channel-dependent payload partition and adaptive embedding. Signal Process 108:642–657

    Article  Google Scholar 

  40. Pareek NK, Patidar V, Sud KK (2006) Image encryption using chaotic logistic map. Image Vis Comput 24:926–934

    Article  Google Scholar 

  41. Pradhan A, Sahu AK, Swain G, Sekhar KR (2016) Performance evaluation parameters of image steganography techniques. In: 2016 international conference on research advances in integrated navigation systems (RAINS). IEEE, Bangalore, pp 1–8

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

    Article  MATH  Google Scholar 

  43. Swain G (2018) Very high capacity image steganography technique using quotient value differencing and LSB substitution. Arab J Sci Eng 44:2995–3004

    Article  Google Scholar 

  44. Tang LL, **e JL, Chen MZ, Xu CY, Zhang R (2019) Image edge detection based on interval type-2 fuzzy similarity. In: 2019 3Rd international conference on electronic information technology and computer engineering (EITCE). IEEE, **amen, pp 958–963

  45. Toony Z, Sajedi H, Jamzad M (2009) A high capacity image hiding method based on fuzzy image coding/decoding. In: 2009 14Th international CSI computer conference. IEEE, Tehran, pp 518–523

  46. Vanmathi C, Prabu S (2018) Image steganography using fuzzy logic and chaotic for large payload and high imperceptibility. Int J Fuzzy Syst 20:460–473

    Article  Google Scholar 

  47. Wang XY, Feng L, Zhao HY (2019) Fast image encryption algorithm based on parallel computing system. Inf Sci 486:340–358

    Article  MATH  Google Scholar 

  48. Wang XY, Gao S (2020a) Image encryption algorithm based on the matrix semi-tensor product with a compound secret key produced by a Boolean network. Inf Sci 539:195–214

    Article  MathSciNet  MATH  Google Scholar 

  49. Wang XY, Gao S (2020b) Image encryption algorithm for synchronously updating Boolean networks based on matrix semi-tensor product theory. Inf Sci 507:16–36

    Article  MathSciNet  MATH  Google Scholar 

  50. Wang FY, Mo H, Zhao L, Li RM (2018) Type-2 fuzzy sets and logic. Tsinghua University Press, Bei**g. in Chinese

    Google Scholar 

  51. Wang CP, Wang XY, **a ZQ, Ma B, Shi YQ (2020) Image description with polar harmonic fourier moments. IEEE Trans Circuits Syst Video Technol 30:4440–4452

    Article  Google Scholar 

  52. Wang XY, Yang L, Liu R, Kadir A (2010) A chaotic image encryption algorithm based on perceptron model. Nonlinear Dynam 62:615–621

    Article  MATH  Google Scholar 

  53. Westfeld A (2001). In: Information hiding workshop. Springer, Berlin, pp 289–302

  54. Wu DC, Tsai WH (2003) A steganographic method for images by pixel-value differencing. Pattern Recogn Lett 24:1613–1626

    Article  MATH  Google Scholar 

  55. **an YJ, Wang XY (2021) Fractal sorting matrix and its application on chaotic image encryption. Inf Sci 547:1154–1169

    Article  MathSciNet  MATH  Google Scholar 

  56. Yuan YH (2016) Research on significance detection method based on RGB visual sensitivity. University of Shandong, Dissertation. in Chinese

    Google Scholar 

  57. Zhang W, Li S (2007) A coding problem in steganography. Des Codes Crypt 46:67–81

    Article  MathSciNet  MATH  Google Scholar 

  58. Zhang X, Wang S (2004) Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security. Pattern Recogn Lett 25:331–339

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 12071179 and Nos. 61972168), the National Natural Science Foundation of Fujian Province (Nos. 2021J01861), Soft Science Research Program of Fujian Province (No. B19085), the Project of Education Department of Fujian Province (No. JT180263), the Youth Innovation Fund of **amen City (3502Z20206020), the Open Fund of Digital Fujian Big Data Modeling and Intelligent Computing Institute, Pre-Research Fund of Jimei University.

Author information

Authors and Affiliations

  1. School of Science, Jimei University, **amen, 361021, China

    Lili Tang & Jialiang **e

  2. School of Mechanical Engineering and Automation, Huaqiao University, **amen, 361021, China

    Lili Tang

  3. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Dongrui Wu

Authors
  1. Lili Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jialiang **e
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongrui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jialiang **e.

Ethics declarations

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interests

All authors declare that they have no conflict of interest.

Additional information

Data accessibility

This paper uses two image databases, which are publicly available on the Internet. As follows:

Barcelona image database(http://www.cvc.uab.es/color_calibration/Database.html)

SIPI image database(http://sipi.usc.edu/database/database.php?volume=misc)

Informed consent

Informed consent was obtained from all individual participants included in the study.

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

Tang, L., **e, J. & Wu, D. An interval type-2 fuzzy edge detection and matrix coding approach for color image adaptive steganography. Multimed Tools Appl 81, 39145–39167 (2022). https://doi.org/10.1007/s11042-022-13127-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13127-0

Keywords

Search

Navigation