# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 1 Introduction
These processes often entail the intricate utilization of prior knowledge and a substantial level of expertise in the domain of image processing. The inherent dependency on prior information culminates in designs that are tailored for specific cases and exhibit a marked lack of adaptability [6]. Such designs are characterized by a uniform application of watermarking patterns across a myriad of images, neglecting the unique content characteristics and quality attributes inherent to each individual image. In the realm of robustness, a significant limitation manifests. Each handcrafted method accentuates a specific attribute or set of attributes, resulting in a fragmented and isolated approach to enhancing watermark robustness. The absence of a comprehensive strategy that encapsulates a broad spectrum of potential attacks is conspicuously evident. For instance, the watermarking technique premised on quantization index modulation is primarily tailored to counter JPEG compression artifacts [10]. Concurrently, methods founded upon the principles of log-polar coordinates are intricately designed to mitigate the impacts of rotational manipulations [11]. This scenario illuminates an overarching challenge--the lack of a holistic, adaptive, and universally applicable watermarking strategy. The juxtaposition of these isolated techniques against the dynamic and multifaceted nature of digital media manipulation threats underscores a significant vulnerability. The evolution of manipulative attacks, characterized by their increasing sophistication, demands a parallel evolution in watermarking techniques that is anchored in adaptability, comprehensive threat mitigation, and contextual applicability. Deep learning [12] is characterized by algorithms inspired by the structure and function of the brain, known as artificial neural networks. These networks are adept at learning from large volumes of data, enabling the extraction of complex patterns and representations. Deep learning has catalyzed significant advancements in various fields, including image and speech recognition, natural language processing, and autonomous systems. The depth of the networks, characterized by multiple layers of interconnected nodes, contributes to their capacity to perform intricate computations, offering superior performance and predictive accuracy in diverse applications. Each layer transforms its input data into increasingly abstract and complex representations, enabling nuanced decision-making and predictions. In the quest for enhanced robustness and adaptability in image watermarking, deep learning emerges as a formidable ally. Unlike their traditional counterparts, deep learning-based watermarking algorithms harbor the potential to learn and adapt [7]. They encapsulate the capacity to intuitively morph in response to the unique attributes of each image and the evolving landscape of threats. This adaptability heralds a new epoch in watermarking--one characterized by enhanced robustness, imperceptibility, and the nuanced balancing of these cardinal attributes. As we traverse this trajectory, the integration of deep learning in watermarking is not just an incremental enhancement but a paradigmatic shift. It propels watermarking from a static, isolated, and case-specific discipline into a dynamic, adaptive, and holistic domain. This evolution is not only pivotal for the enhanced protection of digital media assets but also instrumental in the nuanced balancing of imperceptibility and robustness, ensuring that the integrity and aesthetic value of digital media are meticulously preserved. The necessity of a survey focusing on deep learning-based image watermarking emanates from the rapid advancements and complexities ingrained in this burgeoning field. As the integration of deep learning in image watermarking has emerged as a pivotal focus, there is a requisite for a comprehensive, synthesized, and analytical review of the existing literature and methodologies.