Watermarking One for All: A Robust Watermarking Scheme Against Partial Image Theft
Gaozhi Liu, Silu Cao, Zhenxing Qian, Xinpeng Zhang, Sheng Li, Wanli Peng
Abstract
The proliferation of digital images on the Internet has provided unprecedented convenience, but also poses significant risks of malicious theft and misuse. Digital watermarking has long been researched as an effective tool for copyright protection. However, it often falls short when addressing partial image theft, a common yet little-researched issue in practical applications. Most existing schemes typically require the entire image as input to extract watermarks. However, in practice, malicious users often steal only a portion of the image to create new content. The stolen portion can have arbitrary shape or content, being fused with a new background and may have undergone geometric transformations, making it challenging for current methods to extract correctly. To address the issues above, we propose WOFA (Watermarking One for All), a robust watermarking scheme against partial image theft. First of all, we define the entire process of partial image theft and construct a dataset accordingly. To gain robustness against partial image theft, we then design a comprehensive distortion layer that incorporates the process of partial image theft and several common distortions in channel. For easier network convergence, we employ a multi-level network structure on the basis of the commonly used embedder-distortion layer-extractor architecture and adopt a progressive training strategy. Abundant experiments demonstrate that our superior performance in the scenario of partial image theft, offering a more reliable solution for protecting digital images against unauthorized use in practical use.
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Install the CLIlune papers fulltext 8b601820-a751-402b-83a1-46d1f7524d4fCited by top-tier papers2
- TIACam: Text-Anchored Invariant Feature Learning with Auto-Augmentation for Camera-Robust Zero-WatermarkingAbdullah All Tanvir, Agnibh Dasgupta, Xin ZhongCVPR 2026
- Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable WatermarkingYuheng Li, Weitong Chen, Chengcheng Zhu, Jiale Zhang et al.CVPR 2026
Builds on11
- MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG CompressionZhaoyang Jia, Han Fang, Weiming ZhangACM MM 2021 · 251 citations
- UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field MessagingChaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun et al.NeurIPS 2020 · 198 citations
- PIMoG: An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking NetworkHan Fang, Zhaoyang Jia, Zehua Ma, Ee-Chien Chang et al.ACM MM 2022 · 127 citations
- Towards Blind Watermarking: Combining Invertible and Non-invertible MechanismsRui Ma, Mengxi Guo, Yi Hou, Fan Yang et al.ACM MM 2022 · 100 citations
- Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind WatermarkingChaoning Zhang, Adil Karjauv, Philipp Benz, In So KweonACM MM 2021 · 54 citations
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