Steganalysis of Very Low Embedded JPEG Image in Spatial and Transform Domain Steganographic Scheme Using SVM

  • Deepa D. Shankar
  • Prabhat Kumar Upadhyay
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)


Steganalysis recognizes the manifestation of a hidden message in an artefact. In this paper, the analysis is done statistically, by extracting features that shows a change during an embedding. Machine-learning approach is employed here by using a classifier to identify the stego image and cover image. SVM is used as a classifier and a comparative study is done by using steganographic schemes from spatial plus transform domain. The two steganographic schemes are LSB matching and F5 Six unlike kernel functions, four diverse samplings are used for classification. In this paper, the percentage embedding is kept as low as 10%.


Steganalysis LSB matching F5 SVM Kernel Sampling 


  1. 1.
    Kumar BR, Murti PR (2011) Data security and authentication using steganography. Int J Comput Sci Inf Technol 2(4):1453–1456Google Scholar
  2. 2.
    Nagaraj V, Zayaraz G, Vijayalakshmi V (2011) Modulo based image steganography technique against statistical and histogram analysis. Netw Secur Cryptogr 34–39Google Scholar
  3. 3.
    Christaline JA, Ramesh R, Vaishali D (2014) Steganalysis with classifier combinationsGoogle Scholar
  4. 4.
    Guttikonda JB, Sridevi R (2019) A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images. Multimed Tools Appl 1–19Google Scholar
  5. 5.
    Attaby AA, Alsammak AK, Mursi Ahmed MFM (2018) Data hiding inside JPEG images with high resistance to steganalysis using a novel technique: DCT-M3. Ain Shams Eng J 9(4)CrossRefGoogle Scholar
  6. 6.
    Kalita M, Tuithung T (2015) A comparative study of steganography algorithms of spatial and transform domain. IJCA Proc Natl Conf Recent Trends Inf Technol 9–14Google Scholar
  7. 7.
    Xia Z, Yang L, Xingming S, Sun D, Ruan Z, Liang W (2011) A learning-based steganalytic method against LSB matching steganography. Radioengineering 20Google Scholar
  8. 8.
    Malathi P, Gireeshkumar T (2016) Relating the embedding efficiency of LSB steganography techniques in spatial and transform domains. Procedia Comput Sci 93:878–885CrossRefGoogle Scholar
  9. 9.
    Liu P, Yang C, Liu F, Song X (2015) Improving steganalysis by fusing SVM classifiers for JPEG images. In: International conference on computer science and mechanical automation (CSMA), pp 185–190Google Scholar
  10. 10.
    Bhasin V, Bedi P (2013) Steganalysis for JPEG images using extreme learning machine. In: Proceedings IEEE international conference on systems, man, and cybernetics, SMC, pp 1361–1366Google Scholar
  11. 11.
    Ashu A, Chhikara R (2014) Performance evaluation of first and second order features for steganalysis. Int J Comput Appl 92:17–22CrossRefGoogle Scholar
  12. 12.
    Wang L, Xu Y, Du B, Zhai L, Ren Y (2019) A posterior evaluation algorithm of steganalysis accuracy inspired by residual co-occurrence probability. Pattern Recognit 87:106–117CrossRefGoogle Scholar
  13. 13.
    Hou X, Zhang T, Wu Y, Ji L (2017) Combating highly imbalanced steganalysis with small training samples using feature selection. J Vis Commun Image Represent 49:243–256CrossRefGoogle Scholar
  14. 14.
    Barkana BD, Yildirim B, Saricicek I (2017) Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowl-Based Syst 118:165–176CrossRefGoogle Scholar
  15. 15.
    Castelli M, Vanneschi L, Largo ÁR (2019) Supervised learning: classification. In: Encyclopedia of bioinformatics and computational biology. Elsevier, pp 342–349Google Scholar
  16. 16.
    Shankar D, Shukla V (2019) Effect of principal component analysis in feature based uncalibrated steganalysis using block dependency. SSRN onlineGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Deepa D. Shankar
    • 1
  • Prabhat Kumar Upadhyay
    • 2
  1. 1.Abu Dhabi UniversityAbu DhabiUAE
  2. 2.Department of Electrical and ElectronicsBirla Institute of TechnologyMesraIndia

Personalised recommendations