Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti
Diffusion models have transformed image synthesis through iterative denoising processes that define trajectories from noise to coherent data. While their generative capabilities are widely celebrated, a critical challenge remains unaddressed: ensuring responsible use by verifying whether an image originates from a model's training set, its novel generations, or external sources. We propose a framework that analyzes diffusion trajectories to trace data provenance. Unlike prior methods, we demonstrate that temporal dynamics across the entire trajectory encode discriminative signals for robust classification. This challenges the long-standing "Goldilocks zone" conjecture, which posits that membership inference is effective only within narrow denoising stages. More fundamentally, we expose critical flaws in current membership inference practices, showing how existing methods fail under distribution shifts or when model-generated data is present. For model attribution, we present the first approach applicable to diffusion that avoids foundation models and their potential data leakage. Ultimately, we unify membership inference and model attribution into a single, cohesive framework tailored to modern generative systems, making our assumptions explicit and establishing principled benchmarking standards. Our work prioritizes transparency and accountability in an era of increasingly opaque AI. Code and data given in the Supplementary Material.