ShapeMark: Robust and Diversity-Preserving Watermarking for Diffusion Models

Diffusion models have made substantial advances in recent years, enabling high-quality image synthesis; however, the widespread dissemination and reuse of their outputs have introduced new challenges in intellectual property protection and content provenance. Image watermarking offers a solution to these challenges, and recent work has increasingly explored Noise-as-Watermark (NaW) approaches that integrate watermarking directly into the diffusion process. However, existing NaW methods fail to balance robustness and diversity. We attribute this weakness to value encoding, which encodes watermark bits into individual sampled values. It is extremely fragile in practical application scenarios. To address this, we encode watermark bits into the structured noise pattern, so that the watermark is preserved even when individual values are perturbed. To further ensure generation diversity, we introduce a dedicated randomization design that reshuffles the positions of noise elements without changing their values, preventing the watermark from inducing fixed noise patterns or spatial locations. Extensive experiments demonstrate that our method achieves state-of-the-art robustness while maintaining high generation quality across a wide range of lossy scenarios.

Key Contributions

• Structural Encoding (SE): encodes watermark bits into group-level permutations across multiple noise elements sampled from separable distribution regions, making detection robust to individual value perturbations
• Payload-Debiasing Structural Randomization (PDSR): reshuffles noise element positions post-embedding while preserving values, decoupling payload identity from spatial patterns and maintaining generation diversity
• Demonstrates state-of-the-art watermark robustness under a wide range of lossy post-processing scenarios without degrading image quality or diversity

🛡️ Threat Analysis

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