GeoKR-Net: Geometry-Aware Representation Learning With Knowledge Anchoring for Sensitive Information Detection

Abstract

With the increasing complexity of online communication, sensitive content frequently appears in variant, implicit, and context-dependent forms, posing significant challenges to conventional flat detection approaches. Domain-specific fine-tuning often induces representation anisotropy and semantic feature entanglement, leading to distorted semantic manifolds and unstable decision boundaries. Moreover, existing methods rarely consider the inherent ordinal dependencies among risk levels, limiting their reliability for fine-grained sensitivity assessment. To address these challenges, we propose GeoKR-Net, a hierarchically structured framework that jointly integrates geometric semantic refinement, adaptive knowledge anchoring, and hierarchical risk-aware inference. At the representation level, a geometric refinement operator performs isotropic correction and saliency-guided feature purification, reconstructing a balanced and discriminative semantic manifold. At the knowledge level, a dual-track adaptive anchoring mechanism explicitly models heterogeneous semantic evolution patterns through structured perturbation exploration and latent manifold expansion, enabling the transition from lexical matching to conceptual alignment while maintaining robustness under knowledge-cold-start scenarios. At the decision level, a hierarchical consistency constraint explicitly captures ordinal dependencies among risk levels, ensuring logically coherent and stable multi-level risk prediction. Extensive experiments conducted on four sensitive benchmarks and three general-domain datasets demonstrate that GeoKR-Net consistently outperforms strong baseline models. Further analyses of representation geometry and knowledge coverage verify its effectiveness in mitigating semantic anisotropy, enhancing risk-sensitive feature discrimination, and reducing reliance on static lexicons. The results highlight the importance of jointly modeling geometric structure, adaptive knowledge evolution, and hierarchical semantics for robust and interpretable sensitive information detection.