WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

• URL: http://arxiv.org/abs/2308.10195v2
• Date: Tue, 22 Aug 2023 02:55:39 GMT
• Title: WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning
• Authors: Dongjian Huo, Zehong Zhang, Hanjing Su, Guanbin Li, Chaowei Fang, Qingyao Wu
• Abstract summary: Existing watermark removal methods mainly rely on UNet with task-specific decoder branches. We introduce an implicit joint learning paradigm to holistically integrate information from both branches. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.
• Score: 68.00975867932331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches--one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks; precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multi-scale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.

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