IncreFA: Breaking the Static Wall of Generative Model Attribution
As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive generators appear almost monthly, making existing watermark, classifier and inversion methods obsolete upon release. The core problem lies not in model recognition, but in the inability to adapt attribution itself. We introduce IncreFA, a framework that redefines attribution as a structured incremental learning problem, allowing the system to learn continuously as new generative models emerge. IncreFA departs from conventional incremental learning by exploiting the hierarchical relationships among generative architectures and coupling them with...
| Authors | Haotian Qin, Dongliang Chang, Yueying Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 April 2026 |
| Last updated at source | 21 April 2026 |
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