TrustMark: Robust Watermarking and Watermark Removal for Arbitrary Resolution Images
Tu Bui, Shruti Agarwal, John P. Collomosse
Abstract
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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Install the CLIlune papers fulltext b645138d-5f6d-4f3e-a978-fb8944e0d473Cited by top-tier papers7
- On the Coexistence and Ensembling of WatermarksAleksandar Petrov, Shruti Agarwal, Philip H. S. Torr, Adel Bibi et al.NeurIPS 2025 · 9 citations
- BitMark: Watermarking Bitwise Autoregressive Image Generative ModelsLouis Kerner, Michel Meintz, Bihe Zhao, Franziska Boenisch et al.NeurIPS 2025 · 6 citations
- All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity WatermarkJunjiang Wu, Liejun Wang, Zhiqing GuoCVPR 2026 · 4 citations
- ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token ClusteringDenis Lukovnikov, Andreas Müller, Erwin Quiring, Asja FischerCVPR 2026 · 3 citations
- SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image GenerationJan Kociszewski, Hubert Jastrzebski, Tymoteusz Stepkowski, Filip Manijak et al.ICLR 2026
Builds on20
- A ConvNet for the 2020sZhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer et al.CVPR 2022 · 6,782 citations
- Training Generative Adversarial Networks with Limited DataTero Karras, Miika Aittala, Janne Hellsten, Samuli Laine et al.NeurIPS 2020 · 2,345 citations
- Leveraging Frequency Analysis for Deep Fake Image RecognitionJoel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer et al.ICML 2020 · 848 citations
- Focal Frequency Loss for Image Reconstruction and SynthesisLiming Jiang, Bo Dai, Wayne Wu, Chen Change LoyICCV 2021 · 422 citations
- The Stable Signature: Rooting Watermarks in Latent Diffusion ModelsPierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze et al.ICCV 2023 · 370 citations
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