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• The proposed method that integrates lightweight artifact-aware detectors with Multimodal Large Language Models (MLLMs) via a fuzzy decision tree achieves state-of-the-art accuracy in AI-generated image detection.result 85%
• Existing detection methods that exploit low-level artifacts often lack generalization due to model-specific overfitting.limitation 90%
• Multimodal Large Language Models (MLLMs) lack fine-grained perceptual sensitivity to subtle generation artifacts, making them inadequate as standalone detectors for AI-generated images.limitation 90%
• The detection outputs of MLLMs (high-level semantics) and lightweight detectors (low-level artifacts) are complementary.technical 85%
• The proposed framework uses a fuzzy decision tree to adaptively fuse the heterogeneous outputs (continuous likelihoods or binary decisions) from multiple pretrained lightweight detectors and open-source MLLMs.method 90%
• Detectors trained on GAN-based data (e.g., UnivFD, PatchCraft) achieve high accuracy on GAN images but perform poorly on images from Diffusion Models (DM-based) and in-the-wild categories, highlighting a generalization gap.result 85%
• The malicious use and widespread dissemination of AI-generated images pose a serious threat to the authenticity of digital content.market 95%
• A common practice for adapting MLLMs to AI-generated image detection is fine-tuning them on labeled datasets associated with specific generative models.method 80%

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