METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF METHODS FOR EMBEDDING DIGITAL WATERMARKS

Main Article Content

Igor Ruban
Nataliia Bolohova
Vitalii Martovytskyi
Roman Yaroshevych

Abstract

In recent years, we have seen a significant increase in traffic moving across various networks and channels. The development of technology and global network leads to an increase in the amount of multimedia traffic. To authenticate and avoid abuse, data should be protected with watermarks. This paper discusses various robust and invisible watermarking methods in the spatial domain and the transform domain. The basic concepts of digital watermarks, important characteristics and areas of application of watermarks are considered in detail. The paper also presents the most important criteria for assessing the digital watermark effectiveness. Based on the analysis of the current state of the digital watermarking methods, robustness, imperceptibility, security and payload have been determined as the main factors in most scientific works. Moreover, researchers use different methods to improve / balance these factors to create an effective watermarking system. Our research identified the main factors and new techniques used in modern research. And the assessment of watermark method effectiveness was proposed.

Article Details

How to Cite
Ruban, I., Bolohova, N., Martovytskyi, V., & Yaroshevych, R. (2021). METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF METHODS FOR EMBEDDING DIGITAL WATERMARKS. Advanced Information Systems, 5(3), 112–118. https://doi.org/10.20998/2522-9052.2021.3.15
Section
Methods of information systems protection
Author Biographies

Igor Ruban, Kharkiv National University of RadioElectronics, Kharkiv, Ukraine

Doctor of Technical Sciences, Professor, The first vice-rector

Nataliia Bolohova, Kharkiv National University of RadioElectronics, Kharkiv, Ukraine

postgraduate

Vitalii Martovytskyi, Kharkiv National University of RadioElectronics, Kharkiv, Ukraine

Candidate of Technical Sciences, Аssociate professor, Аssociate professor of Computer Science departments

Roman Yaroshevych, Kharkiv National University of RadioElectronics, Kharkiv, Ukraine

assistant

References

Merlac, V., Smatkov, S., Kuchuk, N. and Nechausov, A. (2018), “Resourses Distribution Method of University e-learning on the Hypercovergent platform”, Сonf. Proc. of 2018 IEEE 9th Int. Conf. on Dependable Systems, Service and Technologies, DESSERT’2018, Kyiv, May 24-27, 2018, pp. 136-140, DOI: http://dx.doi.org/10.1109/DESSERT.2018.8409114.

Van Schyndel, R. G., Tirke,l A. Z. and Osborne, C. F. (1994), “A digital watermark //Proceedings of 1st international conference on image processing” IEEE, Vol. 2, pp. 86-90.

Islam, M. and Laskar, R. H. (2018), “Geometric distortion correction based robust watermarking scheme in LWT-SVD domain with digital watermark extraction using SVM”, Multimedia Tools and Applications, Vol. 11, pp. 14407-14434.

Loukhaoukha, K. (2012), “On the security of digital watermarking scheme based on SVD and tiny-GA”, Journal of Information Hiding and Multimedia Signal Processing, Vol. 3, no 2. – С. 135-141.

Singh, A.K. (2017), “Improved Hybrid Algorithm for Robust and Imperceptible Multiple Watermarking using Digital Images”, Multimedia Tools Applications, Springer, Vol. 76(6), pp. 881–890.

Ruban, I., Bolohova, N., Martovytskyi, V., Lukova-Chuiko, N. and Lebediev, V. (2020), “Method of sustainable detection of augmented reality markers by changing deconvolution”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 9(2), pp. 1113-1120.

Mun, S. M. (2019), “Finding robust domain from attacks: A learning framework for blind watermarking”, Neurocomputing, Vol. 337, pp. 191-202.

Roy, A., Maiti, A. K. and Ghosh, K. (2018), “An HVS inspired robust non-blind watermarking scheme in YCbCr color space”, International Journal of Image and Graphics, Vol. 18, no. 03, DOI: https://doi.org/10.1142/S0219467818500158.

Vaidya, P. and PVSSR, C. M. (2017), “A robust semi-blind watermarking for color images based on multiple decompositions”, Multimedia Tools and Applications, Vol. 76, no. 24, рр. 256.23-256.56.

Ruban, I., Khudov, H., Makoveychuk, O., Khudov, V. and Lishchenko, V. (2020), “The Model and the Method for Forming a Mosaic Sustainable Marker of Augmented Reality”, Proceedings - 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET, pp. 402-406.

Singh, A.K., Kumar, B., Singh, G. and Mohan, A (2017), Medical image watermarking: techniques and applications, Book series on Multimedia Systems and Applications, Springer, ISBN: 978-3319576985.

Mohanty, S.P., Sengupta, A., Guturu, P. and Kougianos, E. (2017), “Everything you want to know about watermarking: From Paper marks to hardware protection”, IEEE Consumer Electronics Magazine, Vol. 6(3), pp. 83–91.

Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, pp. 66-69, DOI: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019.

Moosazadeh, M. and Ekbatanifard, G. (2016), “Robust image watermarking algorithm using DCT coefficients relation in YCoCg-R color space”, 2016 Eighth Int. Conference on Information and Knowledge Technology (IKT), IEEE, pp. 263-267.

Zhong, X. and Shih, F. Y. (2019), “Robust Multibit Image Watermarking Based on Contrast Modulation and Affine Rectification”, Int. J. of Pattern Recognition and Artificial Int., Vol. 33, no. 14. https://doi.org/10.1142/S0218001419540363.

Paikaray, D. and Mustafi, A. (2020), “Genetic Algorithm-Based Image Watermarking Using Multiple Location”, Proc. of the Fourth Int. Conference on Microelectronics, Computing and Communication Systems, Springer, Singapore, pp. 617-627.

Loukhaoukha, K., Nabti, M. and Zebbiche, K. (2014), “A Robust SVD- based Image Watermarking Using a MultiObjective Particle Swarm Optimization”, Opto-Electronics Review, Vol. 22(1), pp. 45–54.

Shen, J.J. and Hsu, P.W. (2008), “A Fragile Associative Watermarking on 2D Barcode for Data Authentication”, International Journal of Network Security, Vol. 7(3), pp. 301–309.

Sachnev, V., Kim, H.J., Nam, J., Suresh, S. and Shi, Y.Q. (2009), “Reversible Watermarking Algorithm Using Sorting and Prediction”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 19(7), pp. 989–999.

Pei, S.C. and Guo, J.M. (2003), “Hybrid Pixel-Based Data Hiding and Block-Based Watermarking for Error-Diffused Halftone Images”, IEEE transactions on Circuits and Systems for Video Technology, Vol. 13(8), pp. 867–884.

Lin, CY (2006), “A Reversible Data Transform Algorithm Using Integer Transform for Privacy Preserving Data Mining”, The Journal of Systems & Software, Vol. 117(7), pp. 104–112.

Zhou, X., Zhang, H. and Wang, C (2018), “A Robust Image Watermarking Technique Based on DWT, APDCBT, and SVD”, Symmetry MDPI, Vol. 10(3), pp. 1–15.