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InfoResearchPeer-reviewed
DRAW: Deep Robustness-Adjustable Watermarking
- Published
- Record updated
Summary
The DRAW paper proposes a two-stage deep watermarking framework that decouples robustness to different distortions so each can be adjusted independently. Stage 1 trains parallel watermark networks per distortion using random orthogonal masks, and stage 2 trains a transformer-based decoder on the frozen embedding network. The authors report superior robustness and adjustability over the state of the art, plus a frequency loss for a better transparency-robustness trade-off.