DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models
2026-07-27 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created DailyBench, a new benchmark to better test how well AI can spot images that are either completely AI-made or have small AI edits. Unlike older tests, DailyBench uses recent AI models and focuses on both full images and subtle object changes. They found that current AI detectors do much worse on DailyBench compared to older tests, showing the need for stronger tools. This benchmark helps researchers improve AI detection in more realistic scenarios.
generative modelsAI-generated imagesimage manipulationbenchmarkfull-image synthesisobject-level editsimage detectionrobustnessfake image detectionimage-conditional models
Authors
Xin Jiang, Hao Tang, Junyao Gao, Meiqi Cao, Fei Shen, Dongming Zhang, Yongdong Zhang
Abstract
Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identifying highly realistic content generated by both modern generation and manipulation pipelines. However, existing detection benchmarks are often built with outdated generative models and primarily emphasize full-image synthesis, creating a growing mismatch between benchmark data and the images encountered in real-world generation and editing scenarios. To bridge this gap, we introduce DailyBench, a high-quality unified benchmark for evaluating whether AI-generated image detectors can generalize across both modern full-image synthesis and object-level manipulation. DailyBench contains two complementary subsets: FakeBench, which includes high-quality images synthesized by recent open-source and commercial generative models, and ManipulationBench, which introduces challenging object-level edits applied to real images using advanced image-conditional models. This design makes DailyBench a realistic testbed for studying both generator-level generalization and manipulation-aware detection under subtle local edits. Experiments on DailyBench reveal substantial robustness gaps in current detectors: methods reporting 91-96% balanced accuracy on GenImage drop to 60-76% on FakeBench and 54-66% on ManipulationBench. These results show that existing detectors remain poorly generalized to realistic synthesis and manipulation, highlighting DailyBench as a rigorous testbed for developing robust and manipulation-aware AI-generated image detection methods. The project is available at https://dailybench.github.io/