A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking
This work provides a structured survey for researchers and practitioners in digital watermarking, but it is incremental as it builds on existing literature by focusing exclusively on deep learning approaches.
This survey tackles the problem of organizing and analyzing deep learning-based image watermarking techniques by introducing a refined categorization and exploring representative methodologies, resulting in a comprehensive overview of the field's current state and future directions.
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.