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

arXiv: https://arxiv.org/abs/2308.04603 · HTML: https://mlsanity.com/paper/2308.04603/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 69 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning](https://mlsanity.com/paper/2108.01775/) (2021)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [Distortion Agnostic Deep Watermarking](https://mlsanity.com/paper/2001.04580/) (2020)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks](https://mlsanity.com/paper/1810.07248/) (2018)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [De-END: Decoder-driven Watermarking Network](https://mlsanity.com/paper/2206.13032/) (2022)
- [A Robust Blind Watermarking Using Convolutional Neural Network](https://mlsanity.com/paper/1704.03248/) (2017)
- [A Brief Survey on Deep Learning Based Data Hiding](https://mlsanity.com/paper/2103.01607/) (2021)
- [Robust Spatial-spread Deep Neural Image Watermarking](https://mlsanity.com/paper/2005.11735/) (2020)
- [A Compact Neural Network-based Algorithm for Robust Image Watermarking](https://mlsanity.com/paper/2112.13491/) (2021)
- [A Robust Image Watermarking System Based on Deep Neural Networks](https://mlsanity.com/paper/1908.11331/) (2019)
- [Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things](https://mlsanity.com/paper/1711.01306/) (2017)
- [A Robust Document Image Watermarking Scheme using Deep Neural Network](https://mlsanity.com/paper/2202.13067/) (2022)
- [A Deep Learning-based Audio-in-Image Watermarking Scheme](https://mlsanity.com/paper/2110.02436/) (2021)
- [Hiding Images into Images with Real-world Robustness](https://mlsanity.com/paper/2110.05689/) (2021)
- [DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding](https://mlsanity.com/paper/2010.10334/) (2020)
- [Convolutional Neural Network Architecture for Recovering Watermark Synchronization](https://mlsanity.com/paper/1805.06199/) (2018)

## Built on by (most cited first, 3 of 3)

- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design](https://mlsanity.com/paper/2508.10065/) (2025)
- [Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning](https://mlsanity.com/paper/2310.05395/) (2023)

## Abstract (arXiv)

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.