[Submitted on 11 Nov 2025 (v1), last revised 20 Jul 2026 (this version, v4)]

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Abstract:A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Current methods learn a single, entangled forgery representation, conflating content-dependent flaws with content-agnostic artifacts, and are further constrained by outdated benchmarks. We propose OmniAID, a novel framework centered on a decoupled Mixture-of-Experts (MoE) architecture that separates: (1) semantic flaws across distinct content domains via Routable Specialized Semantic Experts, and (2) content-agnostic universal artifacts from content-dependent flaws via a Fixed Universal Artifact Expert. A two-stage training strategy first specializes experts independently with domain-specific hard-sampling, then trains 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. We also introduce Mirage, a large-scale, contemporary dataset comprising a modern training set and a challenging test set. Extensive experiments demonstrate that OmniAID surpasses existing detectors, establishing a new standard for AIGI detection against modern, in-the-wild threats. Code is available at this https URL.

Submission history

From: Yuncheng Guo [view email]
[v1] Tue, 11 Nov 2025 16:33:49 UTC (5,504 KB)
[v2] Fri, 28 Nov 2025 08:47:40 UTC (10,298 KB)
[v3] Thu, 28 May 2026 13:42:58 UTC (10,319 KB)
[v4] Mon, 20 Jul 2026 08:31:09 UTC (10,319 KB)