Digital Image Quality Evaluation for Spatial Domain Text Steganography

  • Jasni Mohamad ZainEmail author
  • Nur Imana Balqis Ramli
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)


Steganography is one of the techniques that can be used to hide information in any file types such as audio, image, text and video format. The image steganography is about concealing the hidden data into digital images that alter the pixel of the image. This paper will examine how steganography affect the quality of digital images. Two types of images were selected and different capacities of text documents from 4 kB to 45 kB were used as secret messages. The secret message is embedded in the least significant bits of the images and the distortion is measured using peak signal to noise ratio (PSNR). The results show that for small capacity, it is possible to embed in the second most significant bit (LSB 6) while maintaining a good quality image of more than 30 dB, while for a bigger capacity up to 45 kB, embedding in the fourth least significant bit is possible.


Steganography Spatial Least significant bit 


  1. 1.
    Al-Mazaydeh, W.I.A.: Image steganography using LSB and LSB+ Huffman code. Int. J. Comput. Appl. (0975–8887) 99(5), 17–22 (2014)Google Scholar
  2. 2.
    Liew, S.C., Liew, S.-W., Zain, J.M.: Tamper localization and lossless recovery watermarking scheme with ROI segmentation and multilevel authentication. J. Digit. Imaging 26(2), 316–325 (2013)CrossRefGoogle Scholar
  3. 3.
    Awad, A., Mursi, M.F.M., Alsammak, A.K.: Data hiding inside JPEG images with high resistance to steganalysis using a novel technique: DCT-M3. Ain Shams Eng. J. (2017, in press)Google Scholar
  4. 4.
    Gupta, H., Kumar, P.R., Changlani, S.: Enhanced data hiding capacity using LSB-based image steganography method. Int. J. Emerg. Technol. Adv. Eng. 3(6), 212–214 (2013)Google Scholar
  5. 5.
    Vyas, K., Pal, B.L.: A proposed method in image steganography to improve image quality with LSB technique. Int. J. Adv. Res. Comput. Commun. Eng. 3(1), 5246–5251 (2014)Google Scholar
  6. 6.
    Sharma, P., Kumar, P.: Review of various image steganography and steganalysis techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(7), 152–159 (2016)Google Scholar
  7. 7.
    Chitradevi, B., Thinaharan, N., Vasanthi, M.: Data hiding using least significant bit steganography in digital images. In: Statistical Approaches on Multidisciplinary Research, vol. I, pp. 144–150 (2017). (Chapter 17)Google Scholar
  8. 8.
    Rai, P., Gurung, S., Ghose, M.K.: Analysis of image steganography techniques: a survey. Int. J. Comput. Appl. (0975–8887) 114(1), 11–17 (2015)Google Scholar
  9. 9.
    Jain, R., Boaddh, J.: Advances in digital image steganography. In: International Conference on Innovation and Challenges in Cyber Security, pp. 163–171 (2016)Google Scholar
  10. 10.
    Rafat, K.F., Hussain, M.J.: Secure steganography for digital images meandering in the dark. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 7(6), 45–59 (2016)Google Scholar
  11. 11.
    Al-Farraji, O.I.I.: New technique of steganography based on locations of LSB. Int. J. Inf. Res. Rev. 04(1), 3549–3553 (2017)Google Scholar
  12. 12.
    Badshah, G., Liew, S.-C., Zain, J.M., Ali, M.: Watermark compression in medical image watermarking Using Lempel-Ziv-Welch (LZW) lossless compression technique. J. Digit. Imaging 29(2), 216–225 (2016)CrossRefGoogle Scholar
  13. 13.
    Michael, A.U., Chukwudi, A.E., Chukwuemeka, N.O.: A cost effective image steganography application for document security. Manag. Sci. Inf. Technol. 2(2), 6–13 (2017)Google Scholar
  14. 14.
    Kaur, A., Kaur, R., Kumar, N.: A review on image steganography techniques. Int. J. Comput. Appl. (0975–8887) 123(4), 20–24 (2015)Google Scholar
  15. 15.
    Qin, H., Ma, X., Herawan, T., Zain, J.M.: DFIS: a novel data filling approach for an incomplete soft set. Int. J. Appl. Math. Comput. Sci. 22(4), 817–828 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ainur, A.K., Sayang, M.D., Jannoo, Z., Yap, B.W.: Sample size and non-normality effects on goodness of fit measures in structural equation models. Pertanika J. Sci. Technol. 25(2), 575–586 (2017)Google Scholar
  17. 17.
    Aliman, S., Yahya, S., Aljunid, S.A.: Presage criteria for blog credibility assessment using Rasch analysis. J. Media Inf. Warfare 4, 59–77 (2011)Google Scholar
  18. 18.
    Zamani, N.A.M., Abidin, S.Z.Z., Omar, N., Aliman, S.: Visualizing people’s emotions in Facebook. Int. J. Pure Appl. Math. 118(Special Issue 9), 183–193 (2018)Google Scholar
  19. 19.
    Yusoff, M., Ariffin, J., Mohamed, A.: Discrete particle swarm optimization with a search decomposition and random selection for the shortest path problem. J. Comput. Inf. Syst. Ind. Manag. Appl. 4, 578–588 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Advanced Analytics Engineering Centre, Fakulti Sains Komputer dan MatematikUiTM Selangor (Kampus Shah Alam)Shah AlamMalaysia
  2. 2.Fakulti Sains Komputer dan MatematikUiTM Selangor (Kampus Shah Alam)Shah AlamMalaysia

Personalised recommendations