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.

翻译：本文全面综述了基于深度学习的图像水印技术，该技术通过在载体图像中实现水印的不可见嵌入与提取，旨在融合鲁棒性与适应性。我们梳理了这一跨学科领域的复杂图景，将历史基础、当前创新与未来发展方向相联结。与现有文献不同，本研究仅聚焦于基于深度学习的图像水印，通过三项基础贡献提供深入而精炼的分析。首先，我们引入新的分类体系，将领域划分为嵌入-提取器、作为特征变换的深度网络以及混合方法。该分类体系基于深度学习在各研究中扮演的不同角色，旨在注入清晰性，为读者提供技术洞见与方向指引。其次，我们的探索深入代表性方法，囊括各类别中的多元化研究方向与固有挑战，以提供整合性视角。最后，我们突破既有边界，勾勒新兴前沿领域，为未来研究方向提供详细洞察。