A Web Engineering-based Robust Watermark Restoration and Recognition Method for Protecting Online Video Content

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.