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A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images

  • John Babu GuttikondaEmail author
  • Sridevi R.
Article
  • 23 Downloads

Abstract

The detection of stego images by using the steganalysis approach is one of the demanding task in the recent days. Because, it is used as an importer for the immoral activities by hiding the secrets in the messages. For this secret identification, the traditional works develop various steganographic techniques for steganalysis. But, it has some important drawbacks such as low detection percentage, inefficient results, and increased complexity. In order to solve these issues, this paper introduces a new steganalysis approach with an efficient feature selection and optimization techniques. The aim of this paper is to accurately detect the stego and clean images by implementing an efficient classification algorithm. Initially, a novel Coefficient based Walsh Hadamard Transform along with the Gray Level Co-occurrence Matrix (GLCM) is used for extracting the features of the image. Then, an efficient feature selection technique, namely, Pine Growth Optimization (PGO) is developed to select the optimal features from the extracted features. Finally, the Cross Integrated Machine Learning (CIML) classifier is implemented to classify the stego and clean images. The newness is provided during the feature extraction, selection and classification processes. In experiments, the performance results of the proposed steganalysis is evaluated and compared with the existing approaches by using different measures.

Keywords

Steganography Steganalysis Discrete wavelet transformation (DWT) Fast Walsh Hadamard transform Gray level co-occurrence matrix Pine growth optimization and cross integrated machine learning (CIML) classification 

Notes

References

  1. 1.
    Acharya UD, Kamath PR (2013) A secure and high capacity image steganography technique. arXiv preprint arXiv:1304.3629Google Scholar
  2. 2.
    Alimoradi D, Hasanzadeh M (2014) The effect of correlogram properties on blind steganalysis in JPEG images. J Comput Sec 1Google Scholar
  3. 3.
    Bera S, Sharma M, Sikhar SS, Dwivedi A (2016) An efficient blind steganalysis using higher order statistics for the neighborhood difference matrix. In: Wireless communications, signal processing and networking (WiSPNET), international conference on, pp 211–215CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Böhme R (2010) Principles of modern steganography and steganalysis. Adv Stat Steganalysis:11–77Google Scholar
  6. 6.
    Christaline JA, Ramesh R, Vaishali D (2016) Bio-inspired computational algorithms for improved image Steganalysis. Indian J Sci Technol 9Google Scholar
  7. 7.
    Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for steganalysis of digital images. In: Information forensics and security (WIFS), 2014 IEEE international workshop on, pp 48–53CrossRefGoogle Scholar
  8. 8.
    Desai MB, Patel S, Prajapati B (2016) ANOVA and fisher criterion based feature selection for lower dimensional universal image Steganalysis. Int J Image Proc (IJIP) 10:145Google Scholar
  9. 9.
    Feng B, Weng J, Lu W, Pei B (2017) Steganalysis of content-adaptive binary image data hiding. J Vis Commun Image RepresentGoogle Scholar
  10. 10.
    Goljan M, Fridrich J, Cogranne R (2014) Rich model for steganalysis of color images. In: Information forensics and security (WIFS), 2014 IEEE international workshop on, pp 185–190CrossRefGoogle Scholar
  11. 11.
    Holub V, Fridrich J (2015) Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Trans Inform Forensics Sec 10:219–228CrossRefGoogle Scholar
  12. 12.
    Hou X, Zhang T, Xiong G, Lu Z, Xie K (2014) A novel steganalysis framework of heterogeneous images based on GMM clustering. Signal Process Image Commun 29:385–399CrossRefGoogle Scholar
  13. 13.
    Hussain M, Wahab AWA, Anuar NB, Salleh R, Noor RM (2015) Pixel value differencing steganography techniques: analysis and open challenge. In: Consumer electronics-Taiwan (ICCE-TW), 2015 IEEE international conference on, pp 21–22CrossRefGoogle Scholar
  14. 14.
    Karimi H, Shayesteh MG, Akhaee MA (2015) Steganalysis of JPEG images using enhanced neighbouring joint density features. IET Image Process 9:545–552CrossRefGoogle Scholar
  15. 15.
    Kong X, Feng C, Li M, Guo Y (2016) Iterative multi-order feature alignment for JPEG mismatched steganalysis. Neurocomputing 214:458–470CrossRefGoogle Scholar
  16. 16.
    Lerch-Hostalot D, Megías D (2016) Unsupervised steganalysis based on artificial training sets. Eng Appl Artif Intell 50:45–59CrossRefGoogle Scholar
  17. 17.
    Li X, Li B, Luo X, Yang B, Zhu R (2013) Steganalysis of a PVD-based content adaptive image steganography. Signal Process 93:2529–2538CrossRefGoogle Scholar
  18. 18.
    Li X, Zhang T, Zhang Y, Li W, Li K (2014) A novel blind detector for additive noise steganography in JPEG decompressed images. Multimed Tools Appl 68:1051–1068CrossRefGoogle Scholar
  19. 19.
    Li H, W Luo, X Qiu, J Huang (2015) Identification of image operations based on steganalytic features. arXiv preprint arXiv:1503.04718 Google Scholar
  20. 20.
    Li F, Wu K, Lei J, Wen M, Bi Z, Gu C (2016) Steganalysis over large-scale social networks with high-order joint features and clustering ensembles. IEEE Trans Inform Forensics Sec 11:344–357CrossRefGoogle Scholar
  21. 21.
    Liu J-f, Tian Y-g, Han T, Yang C-f, Liu W-b (2015) LSB steganographic payload location for JPEG-decompressed images. Digital Sign Proc 38:66–76CrossRefGoogle Scholar
  22. 22.
    Mohammadi FG, Abadeh MS (2014) Image steganalysis using a bee colony based feature selection algorithm. Eng Appl Artif Intell 31:35–43CrossRefGoogle Scholar
  23. 23.
    Nouri A, Nazari A (2016) Improving image steganalysis performance using a graph-based feature selection method. Adv Comput Sci: Int J 5:33–39Google Scholar
  24. 24.
    Pathak P, Selvakumar S (2014) Blind image Steganalysis of JPEG images using feature extraction through the process of dilation. Digit Investig 11:67–77CrossRefGoogle Scholar
  25. 25.
    Qian Y, Dong J, Wang W, Tan T (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: 2016 IEEE international conference on image processing (ICIP), pp 2752–2756CrossRefGoogle Scholar
  26. 26.
    Sajedi H (2016) Steganalysis based on steganography pattern discovery. J Inform Sec Appl 30:3–14Google Scholar
  27. 27.
    Song J, Gao L, Nie F, Shen HT, Yan Y, Sebe N (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25:4999–5011MathSciNetCrossRefGoogle Scholar
  28. 28.
    Song J, Guo Y, Gao L, Li X, Hanjalic A, Shen HT (2018) From deterministic to generative: multimodal stochastic RNNs for video captioning. IEEE Trans Neural Netwo Learn Syst:1–12Google Scholar
  29. 29.
    Thai TH, Cogranne R, Retraint F (2014) Statistical model of quantized DCT coefficients: application in the steganalysis of jsteg algorithm. IEEE Trans Image Process 23:1980–1993MathSciNetCrossRefGoogle Scholar
  30. 30.
    Trivedi MC, S Sharma, VK Yadav (2016) Analysis of Several Image Steganography Techniques in Spatial Domain: A Survey, in Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, 84Google Scholar
  31. 31.
    Vyas A, Dudul SV (2015) Study of image Steganalysis techniques. Int J Adv Res Comput Sci 6Google Scholar
  32. 32.
    Wang X, Gao L, Song J, Shen H (2017) Beyond frame-level CNN: saliency-aware 3-D CNN with LSTM for video action recognition. IEEE Sign Proc Lett 24:510–514CrossRefGoogle Scholar
  33. 33.
    Wang X, Gao L, Wang P, Sun X, Liu X (2018) Two-stream 3-D convNet fusion for action recognition in videos with arbitrary size and length. IEEE Trans Multimed 20:634–644CrossRefGoogle Scholar
  34. 34.
    Wing W, He Z-M, Yeung DS, Chan PP (2014) Steganalysis classifier training via minimizing sensitivity for different imaging sources. Inf Sci 281:211–224CrossRefGoogle Scholar
  35. 35.
    Wu A, Feng G, Zhang X, Ren Y (2016) Unbalanced JPEG image steganalysis via multiview data match. J Vis Commun Image Represent 34:103–107CrossRefGoogle Scholar
  36. 36.
    Wu S, Zhong S, Liu Y (2018) Deep residual learning for image steganalysis. Multimed Tools Appl 77:10437–10453CrossRefGoogle Scholar
  37. 37.
    Yang C, Shen J, Peng J, Fan J (2013) Image collection summarization via dictionary learning for sparse representation. Pattern Recogn 46:948–961CrossRefGoogle Scholar
  38. 38.
    Zhang H, Cao Y, Zhao X (2017) A Steganalytic approach to detect motion vector modification using near-perfect estimation for local optimality. IEEE Trans Inform Forensics Sec 12:465–478CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Research Scholar, Department of Computer Science and EngineeringJNTUH UniversityHyderabadIndia
  2. 2.Professor & HoD, Department of Computer ScienceJNTUH College of EngineeringHyderabadIndia

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