TrustMark: Robust Watermarking and Watermark Removal for Arbitrary Resolution Images
Tu Bui, Shruti Agarwal, John P. Collomosse
摘要
Imperceptible digital watermarking is important in copyright protection, misinformation prevention, and responsible generative AI. We propose TrustMark -a watermarking method that leverages a spatio-spectral loss function and a 1×1 convolution layer to enhance encoding quality. Trust-Mark is robust against both in-place and out-of-place perturbations while maintaining image quality above 43 dB. Additionally, we propose ReMark, a watermark removal method designed for re-watermarking, along with a simple yet effective algorithm that enables both TrustMark and Re-Mark to operate across arbitrary resolutions. Our methods achieve state-of-art performance on 3 benchmarks 1 .
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引用它的顶会 Paper7
- On the Coexistence and Ensembling of WatermarksAleksandar Petrov, Shruti Agarwal, Philip H. S. Torr, Adel Bibi 等NeurIPS 2025 · 被引用 9 次
- BitMark: Watermarking Bitwise Autoregressive Image Generative ModelsLouis Kerner, Michel Meintz, Bihe Zhao, Franziska Boenisch 等NeurIPS 2025 · 被引用 6 次
- All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity WatermarkJunjiang Wu, Liejun Wang, Zhiqing GuoCVPR 2026 · 被引用 4 次
- ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token ClusteringDenis Lukovnikov, Andreas Müller, Erwin Quiring, Asja FischerCVPR 2026 · 被引用 3 次
- SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image GenerationJan Kociszewski, Hubert Jastrzebski, Tymoteusz Stepkowski, Filip Manijak 等ICLR 2026
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- Focal Frequency Loss for Image Reconstruction and SynthesisLiming Jiang, Bo Dai, Wayne Wu, Chen Change LoyICCV 2021 · 被引用 422 次
- The Stable Signature: Rooting Watermarks in Latent Diffusion ModelsPierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze 等ICCV 2023 · 被引用 370 次
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