LoRC: Detecting AI-Generated Images via Low-Rank Collapse in the Semantic-Residual Space

Abstract

Modern generators faithfully model macroscopic semantics,producing synthetic images that appear highly realistic. Consequently,decisive forensic cues reside in subtle non-semantic visual discrepancies.To reveal these cues, we revisit AIGI detection from a geometric perspec-tive and identify an architecture-agnostic signature. Specifically, mod-ern generators exhibit low-rank collapse (i.e., rank degeneracy) in thesemantic-residual orthogonal subspace while largely preserving the dom-inant semantic direction. This structural flattening consistently emergesduring the final decoding stage, forming a shared bottleneck across di-verse generator architectures. Motivated by this signature, we proposeLoRC, a framework that decouples semantic dominance to capture thecollapsed residual geometry induced by the generative decoding bottle-neck. Our method improves accuracy by an average of 7.0% across mul-tiple benchmarks and achieves 97.0% accuracy on 39 unseen generators.These results demonstrate strong cross-model generalization and robust-ness, making LoRC a reliable approach for AIGI detection in complexreal-world environments.