RESON: Persistent Watermarking Across Heterogeneous Generative Lineages

AI-generated images are increasingly edited and re-synthesized by downstream generative models, yet existing watermarking evaluations largely focus on single-stage transformations rather than provenance across successive generations. We introduce RESON, a generation-time latent watermark for generative lineage persistence that embeds a keyed, message-bearing carrier once in the source noise and enables detection directly from pixels, without downstream reinsertion, generator modification, or diffusion inversion. We evaluate the method across heterogeneous multi-hop lineages, alternative lineage orderings, conventional perturbations, deliberate generative removal attacks, and varying payload capacities. The watermark remains detectable through repeated cross-model re-synthesis while retaining recoverable source-associated information at increasing lineage depths. Controlled carrier and strength ablations show that persistence is driven by structured carrier content rather than injection energy alone, while generation-quality analysis indicates limited degradation in semantic fidelity. Together, these findings establish generative lineage persistence as a distinct watermarking objective and demonstrate the feasibility of insert-once, detect-later provenance across heterogeneous generative transformations. The source code is available at https://airesearchfolio.github.io/reson.