Not the Scalar, but Its Evolution: Robust Source Attribution for Generated Images

The widespread deployment of latent generative models (LGMs) makes generated image source attribution essential for copyright auditing and data governance, yet practical attribution should remain reliable under common post-processing and platform-induced processing. Existing reconstruction-based attribution methods summarize an image's reconstruction response to a single global *scalar*, such as the reconstruction error or the ratio of two consecutive errors. These summaries discard channel-frequency structure, allowing image content and post-processing variation to obscure source evidence and making attribution after spreading difficult. We instead uncover a more stable source-discriminative cue under post-processing: the frequency-resolved *evolution* of residual energy across repeated applications of the same reconstruction operator. Motivated by this insight, we identify a phenomenon we term ***Spectral Contraction*** and use it to build ***SpCon***, a source-attribution framework that accesses only the target model's VAE. SpCon obtains adjacent residuals through two deterministic reconstructions, builds a spectral-contraction representation from their frequency-resolved relative energy change, and aggregates Student-$t$ response densities for each source-condition pair over a finite set of fitted reference components. The same representation supports three settings according to non-belonging reference availability: none (*Target-Only*, TO; belonging references only), external out-of-domain sources (*External-Reference*, ER), or known in-domain competitors (*Known-Reference*, KR). Across seven LGMs, TO, ER, and KR attain average AUROC/AP of 0.8786/0.6344, 0.9404/0.7886, and 0.9652/0.8626 over the 35 conditions, all outperforming the strongest baseline (0.8154/0.5277), and achieve 0.8581/0.5507, 0.9369/0.7549, and 0.9734/0.8773 on the three real-world social platforms.