Existing deep image watermarking methods follow a fixed embedding-distortion-extraction pipeline, where the embedder and extractor are weakly coupled through a final loss and optimized in isolation. This design lacks explicit collaboration, leaving no structured mechanism for the embedder to incorporate decoding-aware cues or for the extractor to guide embedding during training. To address this architectural limitation, we rethink deep image watermarking by reformulating embedding and extraction as explicitly collaborative components. To realize this reformulation, we introduce a Collaborative Interaction Mechanism (CIM) that establishes direct, bidirectional communication between the embedder and extractor, enabling a mutual-teacher training paradigm and coordinated optimization. Built upon this explicitly collaborative architecture, we further propose an Adaptive Feature Modulation Module (AFMM) to support effective interaction. AFMM enables content-aware feature regulation by decoupling modulation structure and strength, guiding watermark embedding toward stable image features while suppressing host interference during extraction. Under CIM, the AFMMs on both sides form a closed-loop collaboration that aligns embedding behavior with extraction objectives. This architecture-level redesign changes how robustness is learned in watermarking systems. Rather than relying on exhaustive distortion simulation, robustness emerges from coordinated representation learning between embedding and extraction. Experiments on real-world and AI-generated datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches in watermark extraction accuracy while maintaining high perceptual quality, showing strong robustness and generalization.

翻译：现有深度图像水印方法遵循固定的嵌入-失真-提取流程，其中嵌入器与提取器仅通过最终损失函数弱耦合，并在训练中被孤立优化。这种设计缺乏显式协作机制，既未建立结构化机制使嵌入器能够融合解码感知线索，也未让提取器在训练过程中指导嵌入过程。为克服这一架构局限，本文通过将嵌入与提取重构为显式协作组件，重新思考深度图像水印框架。为实现该重构，我们提出协同交互机制（CIM），在嵌入器与提取器之间建立直接的双向通信，实现互教师训练范式与协同优化。在此显式协作架构基础上，我们进一步提出自适应特征调制模块（AFMM）以支持有效交互。AFMM通过解耦调制结构与调制强度，实现内容感知的特征调控，引导水印嵌入至稳定的图像特征，同时在提取过程中抑制宿主干扰。在CIM框架下，两侧的AFMM模块形成闭环协作，使嵌入行为与提取目标保持一致。这种架构层面的重新设计改变了水印系统学习鲁棒性的方式：鲁棒性不再依赖于穷举式的失真模拟，而是源于嵌入与提取之间的协同表征学习。在真实世界与AI生成数据集上的实验表明，所提方法在保持高感知质量的同时，其水印提取精度持续优于现有最优方法，展现出强大的鲁棒性与泛化能力。