Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Fragile Watermarking Based on LBP for Blind Tamper Detection in Images

  • Zhang, Heng (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Chengyou (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xiao (School of Mechanical, Electrical and Information Engineering, Shandong University)
  • Received : 2017.01.26
  • Accepted : 2017.03.04
  • Published : 2017.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

Keywords

References

  1. C. S. Lu and H. Y. M. Liao, "Structural digital signature for image authentication: an incidental distortion resistant scheme," IEEE Transactions on Multimedia, vol. 5, no. 2, pp. 161-173, 2003. https://doi.org/10.1109/TMM.2003.811621
  2. Y. S. Singh, B. P. Devi, and K. M. Singh, "A review of different techniques on digital image watermarking scheme," International Journal of Engineering Research, vol. 2, no. 3, pp. 193-199, 2013.
  3. S. M. Mousavi, A. Naghsh, and S. A. R. Abu-Bakar, "Watermarking techniques used in medical images: a survey," Journal of Digital Imaging, vol. 27, no. 6, pp. 714-729, 2014. https://doi.org/10.1007/s10278-014-9700-5
  4. O. Benrhouma, H. Hermassi, and S. Belghith, "Tamper detection and self-recovery scheme by DWT watermarking," Nonlinear Dynamics, vol. 79, no. 3, pp. 1817-1833, 2015. https://doi.org/10.1007/s11071-014-1777-3
  5. H. M. Al-Otum, "Semi-fragile watermarking for grayscale image authentication and tamper detection based on an adjusted expanded-bit multiscale quantization-based technique," Journal of Visual Communication and Image Representation, vol. 25, no. 5, pp. 1064-1081, 2014. https://doi.org/10.1016/j.jvcir.2013.12.017
  6. C. L. Li, A. H. Zhang, Z. F. Liu, L. Liao, and D. Huang, "Semi-fragile self-recoverable watermarking algorithm based on wavelet group quantization and double authentication," Multimedia Tools and Applications, vol. 74, no. 23, pp. 10581-10604, 2015. https://doi.org/10.1007/s11042-014-2188-7
  7. S. Walton, "Information authentication for a slippery new age," Dr. Dobb's Journal, vol. 20, no. 4, pp. 18-26, 1995.
  8. P. W. Wong, "A public key watermark for image verification and authentication," in Proceedings of IEEE International Conference on Image Processing, Chicago, IL, USA, 1998, pp. 455-459.
  9. C. C. Chang, Y. S. Hu, and T. C. Lu, "A watermarking-based image ownership and tampering authentication scheme," Pattern Recognition Letters, vol. 27, no. 5, pp. 439-446, 2006. https://doi.org/10.1016/j.patrec.2005.09.006
  10. W. C. Chen and M. S. Wang, "A fuzzy c-means clustering-based fragile watermarking scheme for image authentication," Expert Systems with Applications, vol. 36, no. 2, pp. 1300-1307, 2009. https://doi.org/10.1016/j.eswa.2007.11.018
  11. S. Rawat and B. Raman, "A chaotic system based fragile watermarking scheme for image tamper detection," AEU - International Journal of Electronics and Communications, vol. 65, no. 10, pp. 840-847, 2011. https://doi.org/10.1016/j.aeue.2011.01.016
  12. M. Botta, D. Cavagnino, and V. Pomponiu, "A successful attack and revision of a chaotic system based fragile watermarking scheme for image tamper detection," AEU - International Journal of Electronics and Communications, vol. 69, no. 1, pp. 242-245, 2015. https://doi.org/10.1016/j.aeue.2014.09.004
  13. L. Teng, X. Y. Wang, and X. K. Wang, "Cryptanalysis and improvement of a chaotic system based fragile watermarking scheme," AEU - International Journal of Electronics and Communications, vol. 67, no. 6, pp. 540- 547, 2013. https://doi.org/10.1016/j.aeue.2012.12.001
  14. O. Benrhouma, H. Hermassi, A. A. A. El-Latif, and S. Belghith, "Chaotic watermark for blind forgery detection in images," Multimedia Tools and Applications, vol. 75, no. 14, pp. 8695-8718, 2016. https://doi.org/10.1007/s11042-015-2786-z
  15. W. Y. Zhang and F. Y. Shih, "Semi-fragile spatial watermarking based on local binary pattern operators," Optics Communications, vol. 284, no. 16-17, pp. 3904-3912, 2011. https://doi.org/10.1016/j.optcom.2011.04.004
  16. T. Ojala, M. Pietikainen, and D. Harwood, "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions," in Proceedings of the 12th IAPR International Conference on Pattern Recognition, Jerusalem, Israel, 1994, pp. 582-585.
  17. T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
  18. T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  19. Y. J. Zhang, C. L. Zhao, Y. M. Pi, S. H. Li, and S. L. Wang, "Image-splicing forgery detection based on local binary patterns of DCT coefficients," Security and Communication Networks, vol. 8, no. 14, pp. 2386-2395, 2015. https://doi.org/10.1002/sec.721
  20. F. F. Yang, C. Y. Wang, W. Huang, and X. Zhou, "Embedding binary image watermark in DC components of all phase discrete cosine biorthogonal transform," International Journal of Security and Its Applications, vol. 9, no. 10, pp. 125-136, 2015. https://doi.org/10.14257/ijsia.2015.9.10.11
  21. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
  22. J. Fridrich, M. Goljan, and N. Memon, "Cryptanalysis of the Yeung-Mintzer fragile watermarking technique," Journal of Electronic Imaging, vol. 11, no. 2, pp. 262-274, 2002. https://doi.org/10.1117/1.1459449
  23. C. C. Chang, Y. H. Fan, and W. L. Tai, "Four-scanning attack on hierarchical digital watermarking method for image tamper detection and recovery," Pattern Recognition, vol. 41, no. 2, pp. 654-661, 2008. https://doi.org/10.1016/j.patcog.2007.06.003