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

TL;DR

This survey provides an in-depth overview of deep learning-based image watermarking, categorizing methods, analyzing current research, and outlining future directions to advance robustness and adaptability.

Contribution

It introduces a new taxonomy for deep learning-based image watermarking and offers a comprehensive analysis of methodologies and future research directions.

Findings

Proposed a refined categorization of watermarking methods.

Analyzed diverse research directions and challenges.

Outlined emerging frontiers for future research.

Abstract

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.