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：本文全面评述了基于深度学习的图像水印技术。该技术涉及在载体图像中实现水印的隐匿嵌入与提取，旨在达成鲁棒性与适应性的无缝融合。我们深入探讨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紧密关联。与现有文献不同，本研究聚焦于结合深度学习的图像水印技术，通过三项核心贡献呈现深入而简洁的分析。首先，我们提出了一种精细化分类体系，将领域划分为嵌入器-提取器、深度网络特征变换及混合方法三大类别。该分类体系受深度学习在各研究中多元角色的启发，旨在提升清晰度，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研究方法，凝练每一类别中的多样化研究方向和固有挑战，形成统一视角。最后，我们突破现有边界，勾勒新兴前沿领域，为未来研究路径提供详尽洞见。