  This paper presents a comprehensive survey on deep learning-based image
watermarking, a technique that entails the invisible embedding and extraction
of watermarks within a cover image, aiming to offer a seamless blend of
robustness and adaptability. We navigate the complex landscape of this
interdisciplinary domain, linking historical foundations, current innovations,
and prospective developments. Unlike existing literature, our study
concentrates exclusively on image watermarking with deep learning, delivering
an in-depth, yet brief analysis enriched by three fundamental contributions.
First, we introduce a refined categorization, segmenting the field into
Embedder-Extractor, Deep Networks as a Feature Transformation, and Hybrid
Methods. This taxonomy, inspired by the varied roles of deep learning across
studies, is designed to infuse clarity, offering readers technical insights and
directional guidance. Second, our exploration dives into representative
methodologies, encapsulating the diverse research directions and inherent
challenges within each category to provide a consolidated perspective. Lastly,
we venture beyond established boundaries to outline emerging frontiers,
offering a detailed insight into prospective research avenues.
