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CDISS-BEMOS: A New Color Document Image Steganography System Based on Beta Elliptic Modeling of the Online Signature

  • Anissa ZenatiEmail author
  • Wael Ouarda
  • Adel M. Alimi
Conference paper
  • 47 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12026)

Abstract

Based on the Beta elliptic Modeling, a new signature steganography color document image system is proposed in this paper. This system uses the Binary Robust Invariant Scalable Keypoint (BRISK) detector to obtain the potential feature points used for constructing the embedding regions. The Beta elliptic signature is transformed into a secret message bits representation using the Huffman Coding (HC) to increase the performance of our system. Then, the secret message bits are divided into three sub message bits \(\mathrm{m}_\mathrm{R}\), \(\mathrm{m}_\mathrm{G}\), and \(\mathrm{m}_\mathrm{B}\), using the weights \(\upalpha _\mathrm{R}\), \(\upalpha _\mathrm{G}\), and \(\upalpha _\mathrm{B}\), respectively. Finally, each sequence bits is embedded into the corresponding channel, red (R), green (G), and blue (B) by modifying the first Least Significant Bit (LSB) of the embedding regions pixels. The robustness evaluation, quantitative and qualitative experimental results on multiple datasets: L3iDocCopies, LRDE Document Binarization Dataset, and on standard test images, demonstrates that the proposed color document images steganography system in the spatial domain maintains a better visual quality measured by Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Matrix (SSIM), and Human Visual System (HSV) metrics, with relatively less computational complexity, which approves its effectiveness as compared to existing systems.

Keywords

Color document image steganography Beta elliptic Modeling Least Significant Bit Information security 

Notes

Acknowledgment

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

References

  1. 1.
    Abdulrahman, A.K., Ozturk, S.: A novel hybrid DCT and DWT based robust watermarking algorithm for color images. Multimed. Tools Appl. 78, 17027–17049 (2019) CrossRefGoogle Scholar
  2. 2.
    Abraham, J., Paul, V.: An imperceptible spatial domain color image watermarking scheme. J. King Saud Univ.-Comput. Inf. Sci. (2016)Google Scholar
  3. 3.
    Al-Haj, A., Barouqa, H.: Copyright protection of e-government document images using digital watermarking. In: 2017 3rd International Conference on Information Management (ICIM), pp. 441–446. IEEE (2017)Google Scholar
  4. 4.
    Bezine, H., Alimi, A.M., Derbel, N.: Handwriting trajectory movements controlled by a beta-elliptic model. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, p. 1228. IEEE (2003)Google Scholar
  5. 5.
    Burie, J.C., Ogier, J.M., Loc, C.V.: A spatial domain steganography for grayscale documents using pattern recognition techniques. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 9, pp. 21–26. IEEE (2017)Google Scholar
  6. 6.
    Dhieb, T., Njah, S., Boubaker, H., Ouarda, W., Ayed, M.B., Alimi, A.M.: An online writer identification system based on beta-elliptic model and fuzzy elementary perceptual codes. arXiv preprint arXiv:1804.05661 (2018)
  7. 7.
    Dhieb, T., Ouarda, W., Boubaker, H., Alimi, A.M.: Beta-elliptic model forwriter identification from online arabic handwriting. J. Inf. Assur. Secur. 11(5), 263–272 (2016)Google Scholar
  8. 8.
    Dhieb, T., Ouarda, W., Boubaker, H., Halima, M.B., Alimi, A.M.: Online Arabic writer identification based on beta-elliptic model. In: ISDA, pp. 74–79 (2015)Google Scholar
  9. 9.
    García-Soto, R., Hernández-Anaya, S., Nakano-Miyatake, M., Rosales-Roldan, L., Perez-Meana, H.: Sender verification system for official documents based on watermarking technique. In: 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 227–232. IEEE (2013)Google Scholar
  10. 10.
    Gonge, S.S., Ghatol, A.A.: Combined DWT-DCT digital watermarking technique software used for CTS of bank. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 776–783. IEEE (2014)Google Scholar
  11. 11.
    Hamdani, M., El Abed, H., Kherallah, M., Alimi, A.M.: Combining multiple HMMS using on-line and off-line features for off-line Arabic handwriting recognition. In: 10th International Conference on Document Analysis and Recognition (ICDAR 2009), pp. 201–205. IEEE (2009)Google Scholar
  12. 12.
    Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)CrossRefGoogle Scholar
  13. 13.
    Jarraya, I., Ouarda, W., Alimi, A.M.: Deep neural network features for horses identity recognition using multiview horses’ face pattern. In: Ninth International Conference on Machine Vision (ICMV 2016), vol. 10341, p. 103410B. International Society for Optics and Photonics (2017)Google Scholar
  14. 14.
    Lazzez, O., Ouarda, W., Alimi, A.M.: Age, gender, race and smile prediction based on social textual and visual data analyzing. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) ISDA 2016. AISC, vol. 557, pp. 206–215. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-53480-0_21CrossRefGoogle Scholar
  15. 15.
    Lazzez, O., Ouarda, W., Alimi, A.M.: Understand me if you can! Global soft biometrics recognition from social visual data. In: Abraham, A., Haqiq, A., Alimi, A.M., Mezzour, G., Rokbani, N., Muda, A.K. (eds.) HIS 2016. AISC, vol. 552, pp. 527–538. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52941-7_52CrossRefGoogle Scholar
  16. 16.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. IEEE (2011)Google Scholar
  17. 17.
    Li, J., Yu, C., Gupta, B., Ren, X.: Color image watermarking scheme based on quaternion Hadamard transform and Schur decomposition. Multimed. Tools Appl. 77(4), 4545–4561 (2018)CrossRefGoogle Scholar
  18. 18.
    Ltaief, M., Bezine, H., Alimi, A.M.: A neuro-beta-elliptic model for handwriting generation movements. In: 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), pp. 803–808. IEEE (2012)Google Scholar
  19. 19.
    Muhammad, K., Ahmad, J., Rehman, N.U., Jan, Z., Sajjad, M.: CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method. Multimed. Tools Appl. 76(6), 8597–8626 (2017)CrossRefGoogle Scholar
  20. 20.
    Munib, S., Khan, A.: Robust image watermarking technique using triangular regions and Zernike moments for quantization based embedding. Multimed. Tools Appl. 76(6), 8695–8710 (2017)CrossRefGoogle Scholar
  21. 21.
    Nasri, H., Ouarda, W., Alimi, A.M.: ReLiDSS: novel lie detection system from speech signal. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8. IEEE (2016)Google Scholar
  22. 22.
    Nissim, N., Cohen, A., Elovici, Y.: ALDOCX: detection of unknown malicious microsoft office documents using designated active learning methods based on new structural feature extraction methodology. IEEE Trans. Inf. Forensics Secur. 12(3), 631–646 (2016)CrossRefGoogle Scholar
  23. 23.
    Ouarda, W., Trichili, H., Alimi, A.M., Solaiman, B.: Bag of face recognition systems based on holistic approaches. In: 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 201–206. IEEE (2015)Google Scholar
  24. 24.
    Ouarda, W., Trichili, H., Alimi, A.M., Solaiman, B.: Towards a novel biometricsystem for smart riding club. J. Inf. Assur. Secur. 11(4), 201–213 (2016)Google Scholar
  25. 25.
    Pandey, M.K., Parmar, G., Gupta, R., Sikander, A.: Non-blind Arnold scrambled hybrid image watermarking in YCbCr color space. Microsyst. Technol., 1–11 (2018)Google Scholar
  26. 26.
    Patvardhan, C., Kumar, P., Lakshmi, C.V.: Effective color image watermarking scheme using YCbCr color space and QR code. Multimed. Tools Appl., 1–23 (2018)Google Scholar
  27. 27.
    Pham, V.Q., Miyaki, T., Yamasaki, T., Aizawa, K.: Geometrically invariant object-based watermarking using SIFT feature. In: 2007 IEEE International Conference on Image Processing, vol. 5, pp. V–473. IEEE (2007)Google Scholar
  28. 28.
    Prasad, V., Dhavale, S.: H. 264/AVC video protection model based on private cloud for military organisation. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–9. IEEE (2016)Google Scholar
  29. 29.
    Sejpal, S., Shah, N.: Comparative performance analysis of secured LWT-SVD based color image watermarking technique in YUV, YIQ and YCbCr color spaces. Int. J. Comput. Appl. 147(7), 34–40 (2016)Google Scholar
  30. 30.
    Su, Q., Chen, B.: Robust color image watermarking technique in the spatial domain. Soft. Comput. 22(1), 91–106 (2018)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Wang, H.: Communication-resource-aware adaptive watermarking for multimedia authentication in wireless multimedia sensor networks. J. Supercomput. 64(3), 883–897 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS)University of SfaxSfaxTunisia

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