Compositional Fingerprints for Open-Set AI-Generated Image Attribution

AI-generated image attribution is increasingly important for digital forensics, copyright protection, and content accountability. In practice, however, attribution is inherently open-set: generative models evolve rapidly, and test images may come from generators unseen during training. This is particularly challenging because new generators often inherit architectures, reuse generation mechanisms, or share model lineages with known sources, causing their forensic traces to partially overlap. Existing methods typically represent each generator with a holistic fingerprint, making it difficult to distinguish shared traces from source specific organization. We propose compositional source fingerprints, based on the insight that generator identity can be characterized by how reusable latent generative patterns are combined rather than by a single indivisible fingerprint. We instantiate this idea with a Restricted Dynamic Hypergraph (RdH), in which fingerprint atoms represent reusable latent patterns and induce hyperedges among images. Restricted activation constructs compact, image-specific atom combinations, while degree-aware propagation suppresses broadly shared atoms with limited source selectivity. The resulting compositional representations support known-source attribution, unknown rejection, and novel source structure discovery. On five Cross-Era protocols, RdH improves open-set attribution AUROC over the strongest evaluated baselines by 9.89 percentage points, while also substantially improving clustering quality among unseen generators.