构建首个覆盖多类生成图像的统一检测基准，助力AI伪造内容识别研究。
UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization
- 整合文本到图像、图像修复等多类生成模型，覆盖照片与艺术图像。
- 首次系统评估检测方法在跨模型、跨任务下的泛化能力与定位精度。
- 适合关注AI图像安全、检测模型泛化性及评测标准的研究者使用。
随着图像生成模型的快速普及，数字图像的真实性成为重要问题。尽管已有研究提出多种AI生成内容检测方法，但现有基准在覆盖范围上存在局限，未能涵盖多样化的生成模型和图像类别，尤其忽视了端到端图像编辑和艺术图像。为此，我们提出UniAIDet，一个统一且全面的基准，包含摄影类与艺术类图像。该基准涵盖文本到图像、图像到图像、图像修复、图像编辑及深度伪造等多种生成模型。基于UniAIDet，我们对多种检测方法进行了全面评估，并回答了关于泛化能力及检测与定位关系的三个关键研究问题。本基准与分析为未来研究提供了坚实基础。
原文摘要 · Abstract (English)
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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