The Tell-Tale Denoiser: Diffusion Model Attribution via Spectral Geometry Signatures

Attributing a generated image to its source diffusion model is a fundamental challenge in provenance verification and intellectual property protection. This problem is particularly difficult because diffusion models trained on different datasets can converge to similar score functions and thus similar output distributions, making the generated images themselves unreliable as attribution evidence. Existing non-invasive methods either fail on architecturally similar variants or rely on signals that vanish when models share the same autoencoder. We propose Spectral Geometry Signatures (SGS), a non-invasive attribution method that fingerprints each candidate model's denoising behavior rather than its outputs. Our key insight is that a model's score function exhibits a distinctive spectral geometry (how it redistributes energy across spatial frequency bands during denoising) that is intrinsic to the denoiser and invariant to the choice of autoencoder. SGS extracts this signature via frequency-controlled perturbations and standard forward passes, with no inversion, optimization, or generation-time enrollment. Across closely related diffusion checkpoints and models differing in architecture and training procedure, SGS provides highly discriminative attribution signals, remains effective under cross-domain prompt shift, and is stable across the autoencoder choices we evaluate. Our code is available at https://anonymous.4open.science/r/SGS-1B08.