Robust Generative Image Watermarking via Texture-Aware Initial Noise Selection

Robust watermark recovery remains challenging in generative image watermarking under lossy channel distortions. In inversion-based diffusion approaches, we find that initial noise, typically sampled randomly and treated as interchangeable, can substantially affect post-distortion latent recovery, even when the model, prompt, and generation configuration are fixed. More importantly, this variation can be anticipated before transmission: the gray-level co-occurrence matrix (GLCM) homogeneity of the generated image exhibits a consistent positive correlation with latent recovery error under both JPEG compression and additive Gaussian noise. Based on this observation, we propose a texture-aware initial noise selection mechanism that samples candidate initial noises and selects a candidate whose resulting watermarked image exhibits low GLCM homogeneity. The mechanism requires neither modification of the embedding mapping nor retraining of the generative model, and introduces only bounded, controllable sender-side generation overhead. Extensive experiments across multiple existing latent embedding schemes, payload capacities, prompts, and channel distortions demonstrate consistent improvements in payload extraction accuracy, with an average gain of 2.78 percentage points. Our work identifies initial noise selection as an effective design dimension for improving the robustness of inversion-based diffusion watermarking.