Skip to main content

Advertisement

SpringerLink
Log in

An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm

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

Abstract

Steganography is the art and a science of obscuring the presence of communication by hiding content in electronic media and so obscuring the presence of communication from the adversary’s perspective. The goal of spatial adaptive approaches is to incorporate additional information in the image’s edge regions. The portions of the picture with the most changes are prioritized for embedding in these approaches. In contrast, wavelet-based approaches insert in high-frequency sub-bands to resemble the human visual system. The concept described in this study is a hybrid of these two concepts. On the contrary, the coefficients of the wavelet transform’s high-frequency sub-bands are more suited for embedding, since they have bigger coefficients surrounding them and reflect the image’s edge regions. Based on a local neighborhood analysis, an edge intensity criterion is employed to identify the suitable embedding coefficients in this technique. The receiver may also recognize these coefficients and fully extract the encoded data. In the suggested technique, the picture is first blocked, and then each block is given a wavelet transform. Several coefficients are discovered for each high-frequency sub-band, depending on the duration of the data, and then, using a genetic algorithm and the coefficients are chosen from the detected coefficients using a genetic approach to ensure that the resulting stego picture has the maximum PSNR value. The results of the implementation reveal that in the suggested strategy, employing the Integer Wavelet Transform is far more effective than using the Discrete Wavelet Transform. The suggested approach is safe against steganalysis assaults such as PDH analysis, RS, and universal steganalysers, and the quality of the stego picture is better than previous methods like SPAM (Steganography by Printed Arrays by Microbes) and SRM (Spatial Rich Model).

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
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Abedini A, Miri A, Maleki A (2017) Parallel Improved Pulse Coupled Neural Network Application for Edge Detection in Image Processing. Comput. Eng. Inf. Technol. 06(02). https://doi.org/10.4172/2324-9307.1000170

  2. Al-Dmour H, Al-Ani A (2016) A steganography embedding method based on edge identification and XOR coding. Expert Syst. Appl. 46. https://doi.org/10.1016/j.eswa.2015.10.024

  3. Atawneh S, Almomani A, Al Bazar H, Sumari P, Gupta B (2017) Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain. Multimed. Tools Appl. 76(18). https://doi.org/10.1007/s11042-016-3930-0

  4. Banharnsakun A (2018) Artificial bee colony approach for enhancing LSB based image steganography. Multimed. Tools Appl 77(20). https://doi.org/10.1007/s11042-018-5933-5

  5. Chang CC, Tseng HW (2004) A steganographic method for digital images using side match. Pattern Recognit. Lett. 25(12). https://doi.org/10.1016/j.patrec.2004.05.006

  6. Chang CC, Pai PY, Yeh CM, Chan YK (2010) A high payload frequency-based reversible image hiding method. Inf. Sci. (Ny). 180(11). https://doi.org/10.1016/j.ins.2010.01.034

  7. Cheddad A, Condell J, Curran K, Mc Kevitt P (Mar. 2010) Digital image steganography: survey and analysis of current methods. Signal Process 90(3):727–752. https://doi.org/10.1016/J.SIGPRO.2009.08.010

    Article  MATH  Google Scholar 

  8. Chen P-Y, Wu W-E (2009) A Modified Side Match Scheme for Image Steganography. Int. J. Appl. Sci. Eng. 7

  9. Chen WJ, Chang CC, Le THN (2010) High payload steganography mechanism using hybrid edge detector. Expert Syst. Appl. 37(4). https://doi.org/10.1016/j.eswa.2009.09.050

  10. Douglas M, Bailey K, Leeney M, Curran K (2018) An overview of steganography techniques applied to the protection of biometric data. Multimed. Tools Appl. 77(13). https://doi.org/10.1007/s11042-017-5308-3

  11. El Safy RO, Zayed HH, El Dessouki A (2009) An adaptive steganographic technique based on integer wavelet transform. https://doi.org/10.1109/ICNM.2009.4907200

  12. Fakhredanesh M, Rahmati M, Safabakhsh R (2019) Steganography in discrete wavelet transform based on human visual system and cover model. Multimed. Tools Appl. 78(13). https://doi.org/10.1007/s11042-019-7238-8

  13. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  14. Gulve AK, Joshi MS (2015) An image steganography method hiding secret data into coefficients of integer wavelet transform using pixel value differencing approach. Math. Probl. Eng. 2015. https://doi.org/10.1155/2015/684824

  15. Hussain M, Wahab AWA, Bin Idris YI, Ho ATS, Jung KH (2018) Image steganography in spatial domain: A survey. Signal Process. Image Commun. 65. https://doi.org/10.1016/j.image.2018.03.012

  16. Jayasingh RJ, Kumar RJS, Telagathoti DB, Sagayam KM, Pramanik S (2022) Speckle noise removal by SORAMA segmentation in digital image processing to facilitate precise robotic surgery. Int J Reliable Qual E-Healthcare 11(1). https://doi.org/10.4018/IJRQEH.295083

  17. Jeevitha S, Amutha Prabha N (2020) Effective payload and improved security using HMT Contourlet transform in medical image steganography. Health Technol. (Berl). 10(1). https://doi.org/10.1007/s12553-018-00285-1

  18. Kadhim IJ, Premaratne P, Vial PJ (2018) Secure image steganography using dual-tree complex wavelet transform block matching. https://doi.org/10.1109/ICECA.2018.8474616

  19. Kadhim IJ, Premaratne P, Vial PJ (2020) Improved image steganography based on super-pixel and coefficient-plane-selection. Signal Processing 171. https://doi.org/10.1016/j.sigpro.2020.107481

  20. Kalita M, Tuithung T, Majumder S (2019) A new steganography method using integer wavelet transform and least significant bit substitution. Comput J 62(11). https://doi.org/10.1093/comjnl/bxz014

  21. 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 Syst. Appl. 41(14). https://doi.org/10.1016/j.eswa.2014.04.022

  22. Khamrui A, Gupta DD, Ghosh S, Nandy S (2017) A spatial domain image authentication technique using genetic algorithm. Commun Comput Info Sci 776. https://doi.org/10.1007/978-981-10-6430-2_45

  23. Khan S, Bianchi T (2018) Ant colony optimization (ACO) based data hiding in image complex region. Int. J. Electr. Comput. Eng. 8(1). https://doi.org/10.11591/ijece.v8i1.pp379-389

  24. Kieu TD, Chang CC (2011) A steganographic scheme by fully exploiting modification directions. Expert Syst. Appl. 38(8). https://doi.org/10.1016/j.eswa.2011.02.122

  25. Kodovský J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inform Forensics Sec 7(2). https://doi.org/10.1109/TIFS.2011.2175919

  26. Kumar V, Kumar D (2018) A modified DWT-based image steganography technique. Multimed. Tools Appl. 77(11). https://doi.org/10.1007/s11042-017-4947-8

  27. Kumar S, Muttoo SK (2010) Data hiding techniques based on wavelet-like transform and complex wavelet transforms. https://doi.org/10.1109/IPTC.2010.46

  28. Laishram D, Tuithung T (2021) A novel minimal distortion-based edge adaptive image steganography scheme using local complexity: (BEASS). Multimed. Tools Appl. 80(1). https://doi.org/10.1007/s11042-020-09519-9

  29. Lee CF, Chang CC, Xie X, Mao K, Shi RH (2018) An adaptive high-fidelity steganographic scheme using edge detection and hybrid hamming codes. Displays 53. https://doi.org/10.1016/j.displa.2018.06.001

  30. Lu TC, Huang CC (2007) Lossless information hiding scheme based on pixels complexity analysis. https://doi.org/10.1109/SITIS.2007.60

  31. Luo W, Huang F, Huang J (2010) Edge adaptive image steganography based on lsb matching revisited. IEEE Trans. Inf. Forensics Secur. 5(2). https://doi.org/10.1109/TIFS.2010.2041812

  32. Maheswari SU, Hemanth DJ (2015) Frequency domain QR code based image steganography using Fresnelet transform. AEU - Int. J. Electron. Commun. 69(2). https://doi.org/10.1016/j.aeue.2014.11.004

  33. Maheswari SU, Hemanth DJ (2015) Image steganography using hybrid edge detector and ridgelet transform. Def. Sci. J. 65(3). https://doi.org/10.14429/dsj.65.7871

  34. Maniccam SS, Bourbakis N (2004) Lossless compression and information hiding in images. Pattern Recognit. 37(3). https://doi.org/10.1016/j.patcog.2003.08.010

  35. Miri A, Faez K (2017) Adaptive image steganography based on transform domain via genetic algorithm. Optik (Stuttg). 145. https://doi.org/10.1016/j.ijleo.2017.07.043

  36. Miri A, Faez K (2018) An image steganography method based on integer wavelet transform. Multimed. Tools Appl. 77(11). https://doi.org/10.1007/s11042-017-4935-z

  37. Muhammad N, Bibi N, Mahmood Z, Kim DG (2015) Blind data hiding technique using the Fresnelet transform. Springerplus 4(1). https://doi.org/10.1186/s40064-015-1534-1

  38. Muhuri PK, Ashraf Z, Goel S (2020) A Novel Image Steganographic Method based on Integer Wavelet Transformation and Particle Swarm Optimization. Appl. Soft Comput. J. 92. https://doi.org/10.1016/j.asoc.2020.106257

  39. Mukherjee N, Paul G, Saha SK (2021) Two-point FFT-based high capacity image steganography using calendar based message encoding. Inf. Sci. (Ny). 552. https://doi.org/10.1016/j.ins.2020.11.044

  40. Nipanikar SI, Hima Deepthi V, Kulkarni N (2018) A sparse representation based image steganography using Particle Swarm Optimization and wavelet transform. Alexandria Eng. J. 57(4). https://doi.org/10.1016/j.aej.2017.09.005

  41. Pandey BK, Pandey D, Wairya S, Agarwal G, Dadeech P, Dogiwal SR, Pramanik S (2022) Application of integrated steganography and image compressing techniques for confidential information transmission In: Cyber Security and Network Security. Wiley, Eds

  42. Patil V, Patil M (2018) Curvelet based ECG steganography for protection of data. Lecture Notes in Comput Vision Biomech 28

  43. Pevný T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2). https://doi.org/10.1109/TIFS.2010.2045842

  44. Prabha K, Sam S (2021) I. “lifting scheme and Schur decomposition based robust watermarking for copyright protection”. In: Sheth, a., Sinhal, a., Shrivastava, a., Pandey, a.K. (eds) intelligent systems. Algorithms for intelligent systems. Springer. Singapore. https://doi.org/10.1007/978-981-16-2248-9_15

  45. Pramanik S, Bandyopadhyay SK (2014) Image steganography using wavelet transform and genetic algorithm. Int J Innovative Res Advanc Eng 1:1–4

    Google Scholar 

  46. Pramanik S, Bandyopadhyay SK (2014) An innovative approach in steganography. Scholars J Eng Technol:276–280.9

  47. Pramanik S, Suresh Raja S (2019) Analytical study on security issues in steganography. Think-India 22(35):106–114

    Google Scholar 

  48. Pramanik S, Suresh Raja S (2020) A secured image steganography using genetic algorithm. Advan Math: Sci J 9(7):4533–4541

    Google Scholar 

  49. Pramanik S, Singh RP, Ghosh R, Bandyopadhyay SK (2020) A Unique Way to Generate Password at Random Basis and Sending it Using a New Steganography Technique. Indonesian J Electrical Eng Inform 8(3):525–531

    Google Scholar 

  50. Pramanik S, Singh RP, Ghosh R (2020) Application of bi-orthogonal wavelet transform and genetic algorithm in image steganography. Multimed. Tools Appl. 79:25–26. https://doi.org/10.1007/s11042-020-08676-1

    Article  Google Scholar 

  51. Pramanik S, Samanta D, Ghosh R, Bandyopadhyay SK (2021) A New Combinational Technique in Image Steganography. Int J Info Sec Privacy 15(3):4 IGI Global

  52. Sabeti V, Amerei S (2021) Secure and imperceptible image steganography in discrete wavelet transform using the XOR logical function and genetic algorithm. ISC Int. J. Inf. Secur

  53. Sabeti V, S. Samavi, M. Mahdavi, and S. Shirani, “Steganalysis and payload estimation of embedding in pixel differences using neural networks,” Pattern Recognit., vol. 43, no. 1, 2010, https://doi.org/10.1016/j.patcog.2009.06.006.

  54. Sabeti V, Samavi S, Shirani S (2013) An adaptive LSB matching steganography based on octonary complexity measure. Multimed. Tools Appl. 64(3). https://doi.org/10.1007/s11042-011-0975-y

  55. Sajasi S, Eftekhari Moghadam AM (2015) An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method. Appl. Soft Comput. J. 30. https://doi.org/10.1016/j.asoc.2015.01.032

  56. Sarkar M, Pramanik S, Sahnwaj S (2020) Image Steganography Using DES-DCT Technique. Turkish J Comp Math Educ 11(2):906–913

    Google Scholar 

  57. Sathisha N, Priya R, Babu KS, Raja KB, Venugopal KR, Patnaik LM (2013) DTCWT based high capacity steganography using coefficient replacement and adaptive scaling. https://doi.org/10.1117/12.2051889

  58. Shaukat A, Chaurasia M, Sanyal G (2016) A novel image steganographic technique using fast fourier transform. https://doi.org/10.1109/ICRTIT.2016.7569519

    Book  Google Scholar 

  59. Singh S, Siddiqui TJ (2013) Robust image steganography using complex wavelet transform. https://doi.org/10.1109/MSPCT.2013.6782087

  60. Subhedar MS, Mankar VH (2016) Image steganography using redundant discrete wavelet transform and QR factorization. Comput. Electr. Eng. 54. https://doi.org/10.1016/j.compeleceng.2016.04.017

  61. Subhedar MS, Mankar VH (2018) Curvelet transform and cover selection for secure steganography. Multimed. Tools Appl. 77(7). https://doi.org/10.1007/s11042-017-4706-x

  62. Thabit R, Khoo BE (2015) A new robust lossless data hiding scheme and its application to color medical images. Digit. Signal Process. A Rev. J. 38. https://doi.org/10.1016/j.dsp.2014.12.005

  63. Wazirali R, Alasmary W, Mahmoud MMEA, Alhindi A (2019) An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms. IEEE Access 7. https://doi.org/10.1109/ACCESS.2019.2941440

  64. Wu DC, Tsai WH (2003) A steganographic method for images by pixel-value differencing. Pattern Recognit. Lett. 24:9–10. https://doi.org/10.1016/S0167-8655(02)00402-6

    Article  MATH  Google Scholar 

  65. Xiao M, He Z (2015) High capacity image steganography method based on framelet and compressive sensing. In: MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis. 9811, https://doi.org/10.1117/12.2205279

  66. Yadav GS, Ojha A (2018) Hamiltonian path based image steganography scheme with improved imperceptibility and undetectability. Appl. Soft Comput. J. 73. https://doi.org/10.1016/j.asoc.2018.08.034

  67. Zhang X, Wang S (2004) Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security. Pattern Recognit. Lett. 25(3). https://doi.org/10.1016/j.patrec.2003.10.014

Download references

Funding

The author did not receive any funding.

Author information

Authors and Affiliations

  1. Haldia Institute of Technology, Haldia, India

    Sabyasachi Pramanik

Authors
  1. Sabyasachi Pramanik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sabyasachi Pramanik.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pramanik, S. An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm. Multimed Tools Appl 82, 34287–34319 (2023). https://doi.org/10.1007/s11042-023-14505-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14505-y

Keywords

Search

Navigation