A Web Engineering-based Robust Watermark Restoration and Recognition Method for Protecting Online Video Content
DOI:
https://doi.org/10.13052/jwe1540-9589.2441Keywords:
Web video content, copyright protection, robustness indicators, digital watermark, feature point extracting and matchingAbstract
With the rapid expansion of over-the-top (OTT) services and web-based video streaming platforms, copyright protection has become a critical concern. Unauthorized redistribution and modification of digital content via composite transformations and distortions threaten content security. While watermarking and digital rights management (DRM) offer protection, existing methods often fail under real-world web-based attack scenarios. In this paper, we present a web engineering-based robust watermark restoration and recognition method to enhance the security of online video content. Our approach employs AKAZE feature detection to extract robust feature points, while a discrete wavelet transform (DWT) is used for subband decomposition, embedding the watermark in the lowest-energy subband near the detected feature points. To ensure resilience against distortions common in web environments, we evaluate our method under four types of noise (Gaussian, salt-and-pepper, uniform, and Poisson) and four rotation angles (0∘, 90∘, 180∘, and 270∘). AKAZE-based feature matching compensates for rotation distortions, while noise removal is handled using Gaussian, Median, or BM3D filtering. Performance evaluation using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC), and bit error rate (BER) confirms the effectiveness of our method. Results show that BM3D filtering achieves the highest average NC (0.8996) and the lowest BER (0.1137), demonstrating strong robustness against composite transformation attacks. This study contributes to web-based video security by integrating feature-based watermarking techniques with web engineering principles, ensuring effective protection for modern web applications.
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References
C. S. Kim. Copyright Infringement Issue Report: Status of YouTube Fast Movie Channels and Copyright Infringement. Korea Copyright Protection Agency, 2023. [Online]. Available: https://www.kcopa.or.kr/download.do?uuid=ade036e0-7fd4-40c1-a73b-9d6b90f34a6a.pdf. [Accessed: Jan. 31, 2025]
KPMG. A New Change in OTT-Driven Video Platform Industry. 2024. [Online]. Available: https://assets.kpmg.com/content/dam/kpmg/kr/pdf/2024/business-focus/kpmg-korea-video-content-platform-20240927.pdf. [Accessed: Jan. 31, 2025]
KISDI. Recent Trends in Domestic Pay-TV Service Market. 2024. [Online]. Available: https://library.kisdi.re.kr/$/10110/contents/4334696?checkinId=2418989&articleId=1494256. [Accessed: Jan. 31, 2025]
Up to 3 billion compensation will be given to internal reporters of ’illegal videos and webtoons. (2023, October 17). Retrieved from https://www.korea.kr/news/policyNewsView.do?newsId=148921433. [Accessed: Jan. 31, 2025]
V. N. Kirti. A review on digital watermarking and its techniques. IJCSMC, vol. 3, no. 6, pp. 686–690, 2014.
K. J. Lim and T. Y. Choi. Performance comparison of frequency-based watermarking methods. J. Inst. Electron. Eng. Korea-CI, vol. 38, no. 5, pp. 65–76, 2001.
H.-Y. Ryu, G.-W. Lee, and B.-D. Gwon. Noise removal from satellite images using wavelet filter. in Proc. Korean Earth Sci. Soc. Conf., pp. 400–407, 2005.
S. Y. Choi, Y. H. Seo, J. S. Yoo, D. K. Kim, and D. W. Kim. Real-time watermarking algorithm using statistical properties of multi-resolution in DWT-based image compression. J. Korean Inst. Inf. Secur. Cryptol., vol. 13, no. 6, pp. 33–43, 2003. doi:10.13089/JKIISC.2003.13.6.33.
A. Ravishankar, S. Anusha, H. K. Akshatha, A. Raj, S. Jahnavi, and J. Madhura. A survey on noise reduction techniques in medical images. in Proc. IEEE Int. Conf. Electron. Commun. Aerosp. Technol. (ICECA), pp. 385–389, 2017.
R. C. Gonzalez and R. E. Woods. Digital Image Processing. Pearson Education, 2008.
A. Buades, B. Coll, and J. M. Morel. A review of image denoising algorithms, with a new one. Multiscale Model. Simul. vol. 4, no. 2, pp. 490–530, 2005.
E. G. Onyedinma and I. E. Onyenwe. Image Restoration: A Comparative Analysis of Image De noising Using Different Spatial Filtering Techniques. arXiv preprint arXiv:2401.09460, 2024.
A. Buades, B. Coll, and J. M. Morel. A review of image denoising algorithms, with a new one. Multiscale Model. Simul., vol. 4, no. 2, pp. 490–530, 2005. doi:10.1137/040616024.
R. S. Selvi, B. A. Varshini, and S. Deekshiga. A comparative analysis of image denoising filters for salt and pepper noise. in Proc. 2023 Int. Conf. Smart Struct. Syst. (ICSSS), Chennai, India, pp. 1–10, 2023. doi:10.1109/ICSSS58085.2023.10407368.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007. doi:10.1109/TIP.2007.901238.
A. Buades, B. Coll, and J.-M. Morel. A non-local algorithm for image denoising. in Proc. 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR’05), vol. 2, San Diego, CA, USA, pp. 60–65, 2005. doi:10.1109/CVPR.2005.38.
M. Hasan and M. R. El-Sakka. Improved BM3D image denoising using SSIM-optimized Wiener filter. EURASIP J. Image Video Process., vol. 2018, no. 25, pp. 1–12, 2018. doi:10.1186/s13640-018-0264-z.
R. C. Gonzalez, R. E. Woods. Digital Image Processing, 4th ed., Pearson, 2018.
A. Dixit and P. Sharma. A comparative study of wavelet thresholding for image denoising. Int. J. Image Graph. Signal Process., vol. 12, pp. 39–46, 2014. doi:10.5815/ijigsp.2014.12.06.
M. U. Danish. A comparative study of image denoising algorithms. arXiv preprint, arXiv:2412.05490, 2024.
P. F. Alcantarilla and T. Solutions. Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1281–1298, 2011. doi:10.5244/C.27.13.
P. F. Alcantarilla, A. Bartoli, and A. J. Davison. KAZE features. in Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 214–227, 2012. doi:10.1007/978-3-642-33783-3_16.
D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004. doi:10.1023/B:VISI.0000029664.99615.94.
J. E. Lee and Y. T. Shin. Performance evaluation of feature point extraction algorithms for watermark restoration at various angles. J. Korea Softw. Asset Valuation, vol. 20, no. 4, pp. 91–101, 2024, doi:10.29056/jsav.2024.12.10.
O. O. Khalifa, Y. binti Yusof, and R. F. Olanrewaju. Performance evaluations of digital watermarking systems. in Proc. 2012 8th Int. Conf. Inf. Sci. Digit. Content Technol. (ICIDT2012), vol. 3, pp. 533–536, 2012.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. doi:10.1109/TIP.2003.819861.
O. Evsutin and K. Dzhanashia. Watermarking schemes for digital images: Robustness overview. Signal Process. Image Commun., vol. 100, p. 116523, 2022. doi:10.1016/j.image.2021.116523.

