Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

With the rapid advancement of generative models, highly realistic image synthesis has posed new challenges to digital security and media credibility. Although AI-generated image detection methods have partially addressed these concerns, a substantial research gap remains in evaluating their performance under complex real-world conditions. This paper introduces the Real-World Robustness Dataset (RRDataset) for comprehensive evaluation of detection models across three dimensions: 1) Scenario Generalization – RRDataset encompasses high-quality images from seven major scenarios (War \& Conflict, Disasters \& Accidents, Political \& Social Events, Medical \& Public Health, Culture \& Religion, Labor \& Production, and everyday life), addressing existing dataset gaps from a content perspective. 2) Internet Transmission Robustness – examining detector performance on images that have undergone multiple rounds of sharing across various social media platforms.3) Re-digitization Robustness – assessing model effectiveness on images altered through four distinct re-digitization methods.We benchmarked 17 detectors and 10 vision-language models (VLMs) on RRDataset and conducted a large-scale human study involving 192 participants to investigate human few-shot learning capabilities in detecting AI-generated images. The benchmarking results reveal the limitations of current AI detection methods under real-world conditions and underscore the importance of drawing on human adaptability to develop more robust detection algorithms. Our dataset is publicly available under an anonymous link for review purposes: https://zenodo.org/records/14963880.