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

TL;DR

This paper introduces WMFormer++, a nested transformer model that employs implicit joint learning and attention mechanisms to significantly improve visible watermark removal, outperforming existing methods on challenging benchmarks.

Contribution

The paper proposes a novel nested transformer architecture with implicit joint learning and cross-channel attention for more effective watermark removal.

Findings

Outperforms state-of-the-art methods by a large margin

Effective in challenging benchmark scenarios

Demonstrates superior background restoration and watermark localization

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.