A Compreshensive review on Digital Image Watermarking Techniques

With the increasing prevalence of digital media, it is crucial to develop effective methods for transferring hidden data, establishing ownership of digital content, and safeguarding the rights of creators. In this article, we present a comprehensive analysis of watermarking strategies specifically employed in machine learning systems. Our investigation begins with an overview of the foundational principles of digital watermarking, encompassing both traditional practices and advancements facilitated by machine learning. Subsequently, we delve into a discussion of the most widely utilized digital watermarking techniques based on deep learning models. We conduct a comparative analysis of these techniques, examining their strengths and weaknesses. Furthermore, we provide a summary and analysis of the latest contributions to the field, thereby capturing the current state of research. By exploring the realm of watermarking strategies within machine learning, our study aims to shed light on advancements in this field and offer valuable insights into the most effective approaches. Protecting digital content and ensuring the integrity of shared information are critical objectives. Understanding the strengths and limitations of different watermarking techniques can pave the way for the development of more resilient and efficient solutions.