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

TL;DR

OmniAID introduces a decoupled Mixture-of-Experts framework that effectively separates semantic content flaws from universal artifacts, enabling robust detection of AI-generated images across diverse models and real-world scenarios.

Contribution

The paper presents a novel decoupled MoE architecture with a two-stage training strategy and a new large-scale Mirage dataset, improving generalization and robustness in AI-generated image detection.

Findings

Outperforms existing detectors on traditional benchmarks.

Achieves superior generalization on the new Mirage dataset.

Demonstrates robustness against modern in-the-wild AI-generated images.

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representation, conflating content-dependent flaws with content-agnostic artifacts, and are further constrained by outdated benchmarks.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 designed to decouple: (1) semantic flaws across distinct content domains, and (2) 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 novel 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 To address outdated benchmarks and validate real-world applicability, we introduce Mirage, a new large-scale, contemporary dataset.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 and robust standard for AIGI authentication against modern, in-the-wild threats.