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

arXiv — cs.CV•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of OmniAID marks a significant advancement in the field of AI-generated image detection. Traditional methods have struggled with the challenge of distinguishing between content-specific flaws and universal artifacts, leading to limitations in their effectiveness. OmniAID's innovative approach utilizes a decoupled Mixture-of-Experts architecture, allowing it to specialize in detecting semantic flaws across various content domains while simultaneously identifying content-agnostic artifacts. This dual capability is crucial as the prevalence of AI-generated images continues to rise, necessitating more robust detection systems. The framework's bespoke two-stage training strategy further enhances its performance, ensuring that each expert is finely tuned to its specific domain. The introduction of the Mirage dataset, a large-scale contemporary dataset, complements this framework, providing the necessary data for training and evaluation. As AI-generated content becomes more s…
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