Watermarking the initial noise of diffusion models has emerged as a promising approach for image provenance, but content-independent noise patterns can be forged via inversion and regeneration attacks. Recent semantic-aware watermarking methods improve robustness by conditioning verification on image semantics. However, their reliance on a single global semantic binding makes them vulnerable to localized but globally coherent semantic edits. To address this limitation and provide a trustworthy semantic-aware watermark, we propose $\underline{\textbf{S}}$emantic $\underline{\textbf{L}}$atent $\underline{\textbf{I}}$njection via $\underline{\textbf{C}}$ompartmentalized $\underline{\textbf{E}}$mbedding ($\textbf{SLICE}$). Our framework decouples image semantics into four semantic factors (subject, environment, action, and detail) and precisely anchors them to distinct regions in the initial Gaussian noise. This fine-grained semantic binding enables advanced watermark verification where semantic tampering is detectable and localizable. We theoretically justify why SLICE enables robust and reliable tamper localization and provides statistical guarantees on false-accept rates. Experimental results demonstrate that SLICE significantly outperforms existing baselines against advanced semantic-guided regeneration attacks, substantially reducing attack success while preserving image quality and semantic fidelity. Overall, SLICE offers a practical, training-free provenance solution that is both fine-grained in diagnosis and robust to realistic adversarial manipulations.

翻译：对扩散模型的初始噪声添加水印已成为图像溯源的一种前景广阔的方法，但独立于内容的噪声模式可能通过反转和再生攻击被伪造。近期基于语义感知的水印方法通过将验证过程与图像语义绑定，提高了鲁棒性。然而，这些方法依赖于单一的全局语义绑定，使其容易受到局部但全局连贯的语义编辑攻击。为克服这一局限并提供可信的语义感知水印，我们提出了通过分区嵌入进行语义潜在注入（SLICE）。我们的框架将图像语义解耦为四个语义因子（主体、环境、动作和细节），并将其精确锚定到初始高斯噪声中的不同区域。这种细粒度的语义绑定支持高级水印验证，使得语义篡改可检测且可定位。我们从理论上论证了SLICE为何能实现鲁棒且可靠的篡改定位，并提供了误接受率的统计保证。实验结果表明，在应对先进的语义引导再生攻击时，SLICE显著优于现有基线方法，在保持图像质量和语义保真度的同时，大幅降低了攻击成功率。总体而言，SLICE提供了一种实用、无需训练且诊断细粒度、对现实对抗操作具有鲁棒性的溯源解决方案。