# An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

arXiv: https://arxiv.org/abs/2007.02460 · HTML: https://mlsanity.com/paper/2007.02460/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [A Watermark-Conditioned Diffusion Model for IP Protection](https://mlsanity.com/paper/2403.10893/) (2024)
- [Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Space](https://mlsanity.com/paper/2404.00230/) (2024)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [TabularMark: Watermarking Tabular Datasets for Machine Learning](https://mlsanity.com/paper/2406.14841/) (2024)
- [A Compact Neural Network-based Algorithm for Robust Image Watermarking](https://mlsanity.com/paper/2112.13491/) (2021)
- [A Robust Document Image Watermarking Scheme using Deep Neural Network](https://mlsanity.com/paper/2202.13067/) (2022)
- [Deep Learning-based Text-in-Image Watermarking](https://mlsanity.com/paper/2404.13134/) (2024)
- [Deep Learning-based Dual Watermarking for Image Copyright Protection and Authentication](https://mlsanity.com/paper/2502.18501/) (2025)
- [FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge](https://mlsanity.com/paper/2504.03128/) (2025)
- [Sim-to-Real: An Unsupervised Noise Layer for Screen-Camera Watermarking Robustness](https://mlsanity.com/paper/2504.18906/) (2025)
- [AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters](https://mlsanity.com/paper/2511.21216/) (2025)
- [Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps](https://mlsanity.com/paper/2604.09688/) (2026)
- [Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning](https://mlsanity.com/paper/2310.05395/) (2023)
- [DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques](https://mlsanity.com/paper/2407.06552/) (2024)
- [ONRW: Optimizing inversion noise for high-quality and robust watermark](https://mlsanity.com/paper/2601.17388/) (2026)

## Abstract (arXiv)

Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.