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arXiv · 2510.23023

UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization

Huixuan Zhang·Xiaojun Wan
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Abstract

With the rapid proliferation of image generative models, the authenticity of digital images has become a significant concern. While existing studies have proposed various methods for detecting AI-generated content, current benchmarks are limited in their coverage of diverse generative models and image categories, often overlooking end-to-end image editing and artistic images. To address these limitations, we introduce UniAIDet, a unified and comprehensive benchmark that includes both photographic and artistic images. UniAIDet covers a wide range of generative models, including text-to-image, image-to-image, image inpainting, image editing, and deepfake models. Using UniAIDet, we conduct a comprehensive evaluation of various detection methods and answer three key research questions regarding generalization capability and the relation between detection and localization. Our benchmark and analysis provide a robust foundation for future research.

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Huixuan Zhang, Xiaojun Wan. 2025-10-27. UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization. https://arxiv.org/abs/2510.23023

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