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

Huixuan Zhang, Xiaojun Wan

Keywords: AI-Generate Image Detection and Localization; Benchmark

TL;DR: We propose UniAIDet, a unified and universal benchmark for evaluating AI-generated image detection and localization.

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.

Primary Area: datasets and benchmarks

Submission Number: 7197