TrajRing: Taming Trajectory Drift for High-Fidelity Diffusion Watermarking

Diffusion watermarking methods such as Tree-Ring enable provenance verification of diffusion-generated images by embedding watermark patterns in the initial latent noise. Despite their robustness to common image transformations, they inevitably compromise generation fidelity, causing watermarked outputs to deviate from their clean counterparts. Existing optimization-based approaches improve generation fidelity but incur substantial per-image computational overhead. To explore an efficient approach to preserving generation fidelity, we analyze paired clean and watermarked denoising trajectories. We identify watermark-induced trajectory drift: perturbations initially confined to the watermark region induce growing deviations in complementary frequencies during denoising, progressively driving the watermarked trajectory away from its clean counterpart. Motivated by this finding, we propose TrajRing, a training-free framework that suppresses this drift while preserving watermark detectability. At each denoising step, TrajRing performs trajectory-aware spectral projection, retaining watermark-bearing components while projecting complementary components onto a timestep-aligned clean reference trajectory. To complement this spectral constraint with spatial guidance, clean-reference query-key injection reuses queries and keys from the clean reference trajectory while retaining value representations from the watermarked branch. Across two benchmarks, TrajRing improves PSNR by up to 14.91 dB over Tree-Ring while maintaining average detection AUCs of at least 0.98 across clean and transformed images. Evaluations across multiple diffusion backbones further show consistent fidelity improvements. Without per-image optimization, TrajRing runs in 4.77 seconds per image, achieving 24$\times$ and 98$\times$ speedups over PT-Mark and ZoDiac, respectively.