# Practical Deep Dispersed Watermarking with Synchronization and Fusion

arXiv: https://arxiv.org/abs/2310.14532 · HTML: https://mlsanity.com/paper/2310.14532/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Admix: Enhancing the Transferability of Adversarial Attacks](https://mlsanity.com/paper/2102.00436/) (2021)
- [Frequency Domain Model Augmentation for Adversarial Attack](https://mlsanity.com/paper/2207.05382/) (2022)
- [Feature Importance-aware Transferable Adversarial Attacks](https://mlsanity.com/paper/2107.14185/) (2021)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks](https://mlsanity.com/paper/1810.07248/) (2018)
- [Triangle Attack: A Query-efficient Decision-based Adversarial Attack](https://mlsanity.com/paper/2112.06569/) (2021)
- [Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks](https://mlsanity.com/paper/2210.02041/) (2022)
- [LECA: A Learned Approach for Efficient Cover-agnostic Watermarking](https://mlsanity.com/paper/2206.10813/) (2022)
- [Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation](https://mlsanity.com/paper/2203.04607/) (2022)

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

- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [Watermarking Autoregressive Image Generation](https://mlsanity.com/paper/2506.16349/) (2025)
- [Geometric Image Synchronization with Deep Watermarking](https://mlsanity.com/paper/2509.15208/) (2025)
- [SSyncOA: Self-synchronizing Object-aligned Watermarking to Resist Cropping-paste Attacks](https://mlsanity.com/paper/2405.03458/) (2024)
- [ScreenMark: Watermarking Arbitrary Visual Content on Screen](https://mlsanity.com/paper/2409.03487/) (2024)
- [Achieving Resolution-Agnostic DNN-based Image Watermarking: A Novel Perspective of Implicit Neural Representation](https://mlsanity.com/paper/2405.08340/) (2024)
- [RoWSFormer: A Robust Watermarking Framework with Swin Transformer for Enhanced Geometric Attack Resilience](https://mlsanity.com/paper/2409.14829/) (2024)
- [Decoupling Defense Strategies for Robust Image Watermarking](https://mlsanity.com/paper/2602.20053/) (2026)
- [Learning Generalizable and Efficient Image Watermarking via Hierarchical Two-Stage Optimization](https://mlsanity.com/paper/2508.08667/) (2025)
- [TIACam: Text-Anchored Invariant Feature Learning with Auto-Augmentation for Camera-Robust Zero-Watermarking](https://mlsanity.com/paper/2602.18863/) (2026)
- [Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable Watermarking](https://mlsanity.com/paper/2602.21849/) (2026)
- [SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack](https://mlsanity.com/paper/2607.27811/) (2026)
- [SpreadMark: Robust Image Watermarking via Spread-Spectrum Embedding](https://mlsanity.com/paper/2608.03165/) (2026)
- [REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection](https://mlsanity.com/paper/2609.32734/) (2026)
- [Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking](https://mlsanity.com/paper/2610.02010/) (2026)

## Abstract (arXiv)

Deep learning based blind watermarking works have gradually emerged and achieved impressive performance. However, previous deep watermarking studies mainly focus on fixed low-resolution images while paying less attention to arbitrary resolution images, especially widespread high-resolution images nowadays. Moreover, most works usually demonstrate robustness against typical non-geometric attacks (textite.g., JPEG compression) but ignore common geometric attacks (textite.g., Rotate) and more challenging combined attacks. To overcome the above limitations, we propose a practical deep Dispersed Watermarking with Synchronization and Fusion, called proposed. Specifically, given an arbitrary-resolution cover image, we adopt a dispersed embedding scheme which sparsely and randomly selects several fixed small-size cover blocks to embed a consistent watermark message by a well-trained encoder. In the extraction stage, we first design a watermark synchronization module to locate and rectify the encoded blocks in the noised watermarked image. We then utilize a decoder to obtain messages embedded in these blocks, and propose a message fusion strategy based on similarity to make full use of the consistency among messages, thus determining a reliable message. Extensive experiments conducted on different datasets convincingly demonstrate the effectiveness of our proposed proposed. Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28% and 5.93% against single and combined attacks, respectively, and show less file size increment and better visual quality. Our code is available at https://github.com/bytedance/DWSF.