ROBIN++: Unified Copyright Protection and Tamper Localization for Diffusion Models Via Dual-Domain Synergistic Watermarking.

Digital watermarking provides a promising solution for verifying the provenance and integrity of generated content. However, existing methods often suffer from an inherent trade-off between copyright robustness and localization sensitivity due to the entanglement of conflicting signals within a single domain. In this paper, we propose ROBIN++, a paradigm that shifts from single-domain entanglement to a Dual-Domain Synergistic design. Our key insight is to explicitly decouple robustness and fragility during injection by assigning them to distinct domains, while re-coupling them for mutual reinforcement during verification. Specifically, we introduce an Adversarial Optimization Algorithm to inject a frequency-domain copyright watermark during generation, maximizing extraction robustness without visual artifacts. Complementarily, a Content-Aware Perturbation generator is integrated into the latent space to inject the spatial-domain localization watermark, ensuring sensitivity to spatial tampering with minimal interference to the copyright watermark. During verification, we propose a dual-branch synergistic detector that facilitates Frequency-to-Spatial Synergy, where robust spectral priors act as global anchors for local anomaly detection. Conversely, a Spatial-to-Frequency Refinement strategy is employed to rectify corrupted latent features using predicted tamper masks. Extensive experiments show that ROBIN++ significantly outperforms existing approaches in both copyright recovery and tamper localization accuracy, effectively mitigating the long-standing trade-off between the two tasks. Code is available at https://github.com/Hannah1102/ROBIN.