A Study on Image Watermarking in Hybrid Transformed Domain

Abstract

In the fast developing digital environment of today, when enormous amounts of multimedia material are regularly created, shared, and updated across worldwide platforms, the task of verifying the authenticity, ownership, and integrity of digital pictures has become of the utmost importance. Because of the widespread availability of strong editing tools and the ease with which information may be disseminated, digital newlinemedia has become more susceptible to unauthorized use, intentional alteration, and infringement of intellectual property rights. When seen in this light, digital watermarking has emerged as an important technical solution that may be used to protect the authenticity and provenance of visual data. However, the efficacy of watermarking is mainly dependent on its capacity to embed information that is undetectable, safe, and resilient newlineinside the host picture while also preserving resistance to a variety of manipulative assaults. In this thesis, an exhaustive and methodical examination of picture watermarking inside the hybrid transformed domain is carried out. This is an advanced strategy that combines the advantages of numerous transformation approaches in order to maximize the effectiveness of watermark embedding and retrieval. In contrast to traditional newlinewatermarking methods, which operate in a single domain, such as the spatial or frequency domain, hybrid approaches take use of the synergistic effects of mixed transformations. These approaches address the trade-offs that exist between resilience, imperceptibility, and computing complexity. More specifically, the purpose of this research is to investigate the incorporation of Artificial Intelligence (AI)-driven enhancements, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD), which ultimately results in watermarking frameworks that are highly resilient to a wide range of common and complex signal processing operations. The primary objective of this research is to design, develop, and validate hybrid