2025
ICCV
ICCV 2025
TrustMark: Robust Watermarking and Watermark Removal for Arbitrary Resolution Images
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 1x1 convolution layer to enhance encoding quality. TrustMark 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 ReMark to operate across arbitrary resolutions. Our methods achieve state-of-art performance on 3 benchmarks.
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Deep Learning and Machine Learning and Security & Privacy
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio