mlsanityData to 14 Oct 2026

High-Capacity Robust Watermarking Technology for High-Resolution Images

arXiv 2607.17056 ↗First posted 19 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

Most existing watermarking techniques are primarily designed for low-resolution images, with few methods tailored for high-resolution images. Moreover, the embedding capacity is often limited to fixed lengths (e.g., 30, 100, 256 bits, etc.), which struggles to meet practical demands. To address these issues, this paper proposes a high-capacity robust watermarking method for high-resolution images, capable of embedding a watermark of 4 KB (32,768 bits) into images with a resolution of 1024*1024, achieving an embedding rate of 0.0313 bpp. Specifically, this paper adopts a block-wise strategy to effectively embed the watermark into local regions, enabling the network to train and learn normally even under low-resource conditions. The encoder and decoder structures respectively employ a reversible symmetric architecture with three convolutional and three deconvolutional layers, ensuring consistency in the coupling and decoupling of the watermark and image features. Additionally, the loss function combines global and local losses with weighted contributions. By incorporating constraints on the visual quality and robustness of local block regions, the overall imperceptibility and robustness of the image are further enhanced. Extensive experimental results verify that the proposed method is effective and feasible in high-resolution image scenarios with high-capacity watermarking, while demonstrating strong robustness against various noise attacks.

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References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2017Deformable Convolutional Networkscited by 1,488
  6. 2019StegaStamp: Invisible Hyperlinks in Physical Photographscited by 265
  7. 2022Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanismscited by 61
  8. 2023Security and Privacy on Generative Data in AIGC: A Surveycited by 34
  9. 2022De-END: Decoder-driven Watermarking Networkcited by 12
  10. 2025TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivitycited by 7
  11. 2025ARIW-Framework: Adaptive Robust Iterative Watermarking Frameworkcited by 1

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  1. 2025We Can Hide More Bits: The Unused Watermarking Capacity in Theory and in Practice
  2. 2026What Limits Robustness in Deep Image Watermarking: An Analysis of Mechanisms and Their Scaling Across Capacities ↗
  3. 2022Go Wide or Go Deep: Levering Watermarking Performance with Computational Cost for Specific Images ↗
  4. 2021Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps
  5. 2026Optimizing and Securing the Modern Watermarking Channel for Images
  6. 2024Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions
  7. 2024SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution
  8. 2022Robust and Large-Payload DNN Watermarking via Fixed, Distribution-Optimized, Weights