Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Selective Image Authentication Using Shearlet Coefficients Tolerant to JPEG Compression

DOI: http://dx.doi.org/10.15439/2017F177

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 681688 ()

Full text

Abstract. A novel selective image authentication system based on the robust digital watermarking and image content describing by shearlet coefficients is proposed. The discrete shearlet transform is performed in order to extract feature vector from the image. Cone-adapted version of the discrete shearlet transform which allows to calculate coefficients more precisely and to cover all $\mathbb{R}^2$ is used. Proposed approach allows to use conventional cryptographic digital signature for the image feature vector verification and makes proposed scheme more secure. In order to embed watermark (WM) into the image, HL3 and LH3 areas of the Haar wavelet transform coefficients are used. Experimental results show that the proposed selective image authentication system is effective in terms of tolerance to JPEG compression, malicious image tampering detection and visual image quality just after embedding.


  1. A. A. J. Menezes, P. Van Oorschot, and S. Vanstone, Handbook of Applied Crytography, ser. Discrete Mathematics and Its Applications Series. Crc Press, 1997.
  2. A. Haouzia and R. Noumeir, “Methods for image authentication: A survey,” Multimedia Tools Appl., vol. 39, no. 1, pp. 1–46, Aug. 2008. [Online]. Available: http://dx.doi.org/10.1007/s11042-007-0154-3
  3. M. H. Lee, V. I. Korzhik, G. Morales-Luna, S. Lusse, and E. Kurbatov, “Image authentication based on modular embedding.” IEICE Transactions, vol. 89-D, no. 4, pp. 1498–1506, 2006.
  4. M. Goljan, J. J. Fridrich, and R. Du, “Distortion-free data embedding for images,” in Proceedings of the 4th International Workshop on Information Hiding, ser. IHW ’01. London, UK, UK: Springer-Verlag, 2001, pp. 27–41.
  5. X.-y. Wang, L.-m. Hou, and J. Wu, “A feature-based robust digital image watermarking against geometric attacks,” Image Vision Comput., vol. 26, no. 7, pp. 980–989, Jul. 2008. [Online]. Available: http://dx.doi.org/10.1016/j.imavis.2007.10.014
  6. M. Alghoniemy and A. H. Tewfik, “Geometric invariance in image watermarking,” IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 145–153, Feb 2004.
  7. S. Shefali and S. M. Deshpande, “Moment invariants for digital image authentication and authorization,” in 2007 International Conference on Control, Automation and Systems, Oct 2007, pp. 1296–1300.
  8. H. M. Al-Otum, “Color image authentication using a zone-corrected error-monitoring quantization-based watermarking technique,” Optical Engineering, vol. 55, no. 8, p. 083103, 2016.
  9. A. Zhuvikin, V. Korzhik, and M.-L. Guillermo, “Semi-fragile image authentication based on CFD and 3-bit quantization,” Indian Journal of Science and Technology, vol. 9, no. 48, 2017.
  10. E. Maiorana, P. Campisi, and A. Neri, “Signature-based authentication system using watermarking in the ridgelet and radon-dct domain,” pp. 67 410I–67 410I–12, 2007. [Online]. Available: http://dx.doi.org/10.1117/12.738013
  11. G. K. Wallace, “The jpeg still picture compression standard,” IEEE Trans. on Consum. Electron., vol. 38, no. 1, pp. xviii–xxxiv, Feb. 1992. [Online]. Available: http://dx.doi.org/10.1109/30.125072
  12. G. Kutyniok and D. Labate, Shearlets: Multiscale Analysis for Multi-variate Data. Birkhauser Mathematics, 2012.
  13. Y. Qu, X. Mu, L. Gao, and Z. Liu, Facial Expression Recognition Based on Shearlet Transform. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 559–565. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-29387-0_86
  14. P. Porwik and A. Lisowska, “The Haar wavelet transform in digital image processing: its status and achievements.” Int. Journal Machine Graphics & Vision., vol. 13, no. 1, pp. 79–98, 2004.
  15. S. Hauser, “Fast finite shearlet transform: A tutorial,” 2011, university of Kaiserslautern, Preprint.
  16. I. Amidror, Mastering the Discrete Fourier Transform in One, Two or Several Dimensions: Pitfalls and Artifacts, 1st ed. Springer Publishing Company, Incorporated, 2015.
  17. N. A. Dodgson, “Image resampling,” University of Cambridge, Com- puter Laboratory, Tech. Rep. UCAM-CL-TR-261, 1992.
  18. F. Ahmed and M. Y. Siyal, A Robust and Secure Signature Scheme for Video Authentication. 2007 IEEE, International Conference on Multimedia and Expo, 2007.
  19. R. G. Gallager, “Low-density parity-check codes,” 1963.
  20. G. V., “Iterative decoding of low-density parity check codes (a survey),” eprint https://arxiv.org/abs/cs/0610022, 2006.
  21. Q. Huynh-Thu and M. Ghanbari, “Scope of validity of PSNR in image/video quality assessment,” Electronics letters, vol. 44, no. 13, pp. 800–801, 2008.
  22. E.-M. A. Mohammadi P. and S. Sh., “Subjective and objective quality assessment of image: A survey,” 2014.