Semantic watermarking methods, embedding identity into the initial latent noise, provide an imperceptible identity traceability for diffusion models in copyright protection and source verification. However, existing methods are highly vulnerable to adversarial attacks, especially geometric transformations (e.g., rotation, cropping) and latent-space manipulations via proxy models, limiting the reliability of watermark verification in practical deployment. To address this issue, we propose a robust and fully reversible, flow-based watermarking framework with dual encoding paths, which preserves high visual fidelity of watermarked image while ensuring resilient identity recovery under adversarial attacks. Specifically, a dual-path network is proposed to encode watermark information into both the generated image and the owner’s secret key. This network leverages Mutual Information Redundancy to recover compromised information under single-path attack, ensuring robust verification. To enhance verification credibility without degrading generation quality, we introduce a joint training strategy that suppresses false positives on negative samples through contrastive learning under fidelity constraints. Furthermore, we employ a backward Euler iteration scheduler for rectified flow models, which facilitate accurate inversion mapping, to enable effective watermark verification, which accurate inversion. Extensive experiments show that our method achieves superior robustness against various adversarial attacks while maintaining high visual quality across diverse generative models.