Research the efficiency of image processing algorithms in zero watermark schemes
DOI:
https://doi.org/10.30837/2522-9818.2025.1.102Keywords:
image; zero watermark; algorithm; authenticationAbstract
Subject matter: image transformation algorithms used in zero-watermarking techniques for development authentication algorithm. Objectives: identify effective image transformation algorithms for use in zero-watermarking schemes. The study addresses the following tasks: examining the range of existing image transformation algorithms, formulating informal requirements for image transformation algorithms to be used in zero-watermarking-based authentication schemes, and making assumptions about the feasibility of using each analyzed image transformation algorithm. To achieve these objectives, the following methods are employed: modeling – software implementation of each studied algorithm, empirical methods – application of algorithms and observation of transformation results, mathematical methods – calculation of normal correlation metrics and peak signal-to-noise ratio (PSNR). Results: an analysis was conducted on a set of algorithms that could potentially be used in zero-watermarking schemes for authentication purposes. A methodology was developed to evaluate algorithms while considering image dimensionality reduction due to compression. Additionally, requirements for image processing algorithms in zero-watermarking-based authentication were established. Conclusions: the study identified the most effective image transformation algorithms for use in zero-watermarking authentication schemes: DWT (Discrete Wavelet Transform), SVD (Singular Value Decomposition), DCT (Discrete Cosine Transform), and K-means clustering. For low-resolution images, DCT is a viable option. The most effective algorithm combinations are DWT + DCT and DWT + K-means, as these combinations ensure optimal robustness to noise while maintaining sensitivity to distinguish similar images. Future authentication schemes based on these algorithms may be useful (in combination with IoT devices), including for user authentication in enterprises and organizations.
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Liu, X., Lou, J., Wang, Y., Du, J., Zou, B. Chen, Y. (2018), "Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images". Proceedings of the SPIE, Volume 10579, DOI: https://doi.org/10.1117/12.2292852
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Li, F. Wang, Z. (2024), "A zero-watermarking algorithm based on vortex-like texture feature descriptors", Electronics, 13, 3906. DOI: https://doi.org/10.3390/electronics13193906
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Roček, A., Javorník, M., Slavíček, K. (2021), "Zero watermarking: Critical analysis of its role in current medical imaging", Journal of Digital Imaging, 34, Р. 204–211. DOI:10.1007/s10278-020-00396-0
Poddubnyi, V., Gvozdov, R., Sievierinov, O., Sukhoteplyi, V., Bulba, S. Lysytsia, D. (2024), "A zero-watermarking algorithm for use in RGB and monochrome images", 2024 IEEE 5th KhPI Week on Advanced Technology (KhPIWeek), Р. 1-5. DOI: https://doi.org/10.1109/KhPIWeek61434.2024.10878081
Nadipally, M. (2019), "Optimization of methods for image-texture segmentation using ant colony optimization", Intelligent Data-Centric Systems: Intelligent Data Analysis for Biomedical Applications, Academic Press, Р. 21-47. DOI:10.1016/B978-0-12-815553-0.00002-1
"The USC-SIPI Image Database". available at: https://sipi.usc.edu/database/
"NASA Image and Video Library". available at: https://images.nasa.gov/
"Accord Framework". available at: http://accord-framework.net
"Emgu CV. Main Page". available at: https://www.emgu.com/
"Math.NET Numerics". available at: https://numerics.mathdotnet.com/
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