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This paper presents a comprehensive survey on deep learning-based imagewatermarking, a technique that entails the invisible embedding and extractionof watermarks within a cover image, aiming to offer a seamless blend ofrobustness and adaptability. We navigate the complex landscape of thisinterdisciplinary domain, linking historical foundations, current innovations,and prospective developments. Unlike existing literature, our studyconcentrates exclusively on image watermarking with deep learning, deliveringan in-depth, yet brief analysis enriched by three fundamental contributions.First, we introduce a refined categorization, segmenting the field intoEmbedder-Extractor, Deep Networks as a Feature Transformation, and HybridMethods. This taxonomy, inspired by the varied roles of deep learning acrossstudies, is designed to infuse clarity, offering readers technical insights anddirectional guidance. Second, our exploration dives into representativemethodologies, encapsulating the diverse research directions and inherentchallenges 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.

A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking