UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917045022490624 |
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| author | Zhang, Huixuan Wan, Xiaojun |
| author_facet | Zhang, Huixuan Wan, Xiaojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization Zhang, Huixuan Wan, Xiaojun Computer Vision and Pattern Recognition Computation and Language 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. |
| title | UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2510.23023 |