OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild

• URL: http://arxiv.org/abs/2511.08423v1
• Date: Wed, 12 Nov 2025 01:58:18 GMT
• Title: OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild
• Authors: Yuncheng Guo, Junyan Ye, Chenjue Zhang, Hengrui Kang, Haohuan Fu, Conghui He, Weijia Li,
• Abstract summary: A universal AI-generated image detector must simultaneously generalize across diverse generative models and varied semantic content.<n>We propose OmniAID, a novel framework centered on a decoupled Mixture-of-Experts architecture.<n>Our model surpasses existing monolithic detectors, establishing a new, robust standard for AIGI authentication against modern, in-the-wild threats.
• Score: 40.585896860010656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Current state-of-the-art methods learn a single, entangled forgery representation--conflating content-dependent flaws with content-agnostic artifacts--and are further constrained by outdated benchmarks. To overcome these limitations, we propose OmniAID, a novel framework centered on a decoupled Mixture-of-Experts (MoE) architecture. The core of our method is a hybrid expert system engineered to decouple: (1) semantic flaws across distinct content domains, and (2) these content-dependent flaws from content-agnostic universal artifacts. This system employs a set of Routable Specialized Semantic Experts, each for a distinct domain (e.g., human, animal), complemented by a Fixed Universal Artifact Expert. This architecture is trained using a bespoke two-stage strategy: we first train the experts independently with domain-specific hard-sampling to ensure specialization, and subsequently train a lightweight gating network for effective input routing. By explicitly decoupling "what is generated" (content-specific flaws) from "how it is generated" (universal artifacts), OmniAID achieves robust generalization. To address outdated benchmarks and validate real-world applicability, we introduce Mirage, a new large-scale, contemporary dataset. Extensive experiments, using both traditional benchmarks and our Mirage dataset, demonstrate our model surpasses existing monolithic detectors, establishing a new, robust standard for AIGI authentication against modern, in-the-wild threats.

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