Generalizable AI-Generated Image Detection via Neighborhood Dependency Representation and Hierarchical Dependency Modeling

Existing AI-generated image detectors often degrade on unseen generators because synthesis traces exhibit substantial variations across generation paradigms. Most existing methods focus on generator-specific artifacts or model forensic evidence from a single perspective, limiting their ability to capture transferable synthesis traces. We observe that although artifact appearances differ, different generators consistently alter image dependency structures across spatial and spectral scales. Based on this observation, we propose a hierarchical dependency modeling framework for generalizable AI-generated image detection. Specifically, we introduce the Neighborhood Dependency Representation (NDR), which reconstructs each pixel from its local context and extracts residual dependency variations to reduce semantic bias and reveal synthesis-related irregularities. Building upon NDR, the local subband dependency branch and global spectral dependency branch capture complementary local and global dependency structures, respectively. A bidirectional confidence guided fusion module further adaptively integrates these representations for robust classification. Extensive experiments on AIGCDetectBenchmark, ForenSynths, and WildRF demonstrate superior cross-generator and real-world generalization, showing the effectiveness of dependency-based forensic representation learning.