Can Heterogeneous Artifacts Coexist? RGSA: Role-Specialized Global–Selective Adaptation for Mitigating Artifact Feature-Space Collapse in Unified Fake Image Detection

Unified Fake Image Detection (FID) aims to detect forgeries across four image forensic domains within a single model. However, joint multi-domain training substantially degrades the discriminative capability originally learned for individual domains, leading to a collapse of the artifact feature space. To uncover the underlying mechanism, we quantify heterogeneous conflicts during joint optimization and establish an evidence chain linking artifact heterogeneity to feature-space collapse. Our analysis reveals that cross-domain updates exhibit mixed and asymmetric interactions, indicating that different artifacts have distinct sharing scopes. We further find that organizing feature-space capacity according to these sharing scopes can effectively mitigate such collapse. Building on these findings, we propose Role-Specialized Global-Selective Adaptation (RGSA), which organizes the adaptation capacity into two pathways with distinct roles to accommodate different sharing scopes. The global pathway captures representations shared across all domains, whereas the selective pathway models partially shared and domain-specific artifacts. Under the four-domain unified protocol of OpenMMSec, RGSA effectively mitigates cross-domain conflicts and artifact feature-space collapse, and outperforms 23 state-of-the-art methods, validating both the proposed mechanism and the role-specialized design.