PreFix: Robust Inversion-Based Image Watermarking via Generation-Side Pre-Compensation

Robustness to distortions is essential for reliable generative image watermarking. Among existing approaches, inversion-based methods offer a promising solution by embedding watermarks into the initial noise latent and recovering them through inversion. However, distortions can perturb the inversion input and compromise watermark recovery. To address this, we take a generation-side perspective on robustness, accounting for distortion effects on watermark extraction before image release. Building on this insight, we propose PreFix, a generation-side pre-compensation framework that optimizes an imperceptible residual per image to improve robustness without modifying the underlying inversion algorithm. Since non-geometric degradations perturb image statistics while geometric transformations introduce spatial misalignment, PreFix combines two complementary mechanisms within a unified residual optimization. Specifically, (1) degradation-pool optimization improves watermark recovery under sampled non-geometric degradations; and (2) alignment-aware optimization uses a lightweight alignment predictor to guide the residual to support transformation estimation and pre-inversion correction. Extensive experiments across watermarking methods and generative models demonstrate substantial robustness gains under both distortion types.