Beyond Single Watermarks: Scalable Adaptive Tree-Ring Watermark Coexistence for Sequential Editing

Digital watermarks are essential for provenance tracking, ownership attribution, and misuse deterrence in AI-generated imagery. In realistic creative pipelines, images often undergo multiple editing rounds, where new watermarks may need to be embedded while earlier provenance signals remain detectable. Existing coexistence studies mainly focus on training-based encoder-decoder schemes, leaving training-free Tree-Ring-style watermarks underexplored despite their capacity, model compatibility, and deployment flexibility. In this paper, we study multi-watermark coexistence for continual image editing based on Tree-Ring-style watermarks. We introduce SeqEdit-BM, a multi-round editing benchmark for evaluating whether multiple watermarks can be repeatedly embedded, preserved, and independently detected across sequential edits. Using this benchmark, we show that Tree-Ring-style watermarks have strong intrinsic compatibility but suffer progressive attenuation during iterative diffusion reconstruction. We attribute this attenuation to trajectory mismatch between watermark injection and detection, and propose an adaptive watermarking method based on trajectory alignment to calibrate the watermark template before injection. We further formulate two allocation protocols for multi-watermark coexistence: single-slot multiplexing, where multiple marks share one spatial-spectral slot, and deterministic slot assignment, where marks are assigned to disjoint latent-frequency slots. Experimental results on verification and identification tasks demonstrate that our method substantially improves multi-watermark coexistence and robustness against sequential editing, sustaining a near-perfect detection rate after five consecutive rounds while preserving image quality with no perceptible artifacts. These results suggest that Tree-Ring-style watermarks offer a practical foundation for provenance preservation in continual image editing.