GenSyn10: A Multi-Generative AI Dataset for Benchmarking Image Classification and Synthetic Image Detection
Researchers have introduced GenSyn10, a synthetic image dataset aligned with CIFAR-10, comprising 60,000 images generated by three architecturally diverse state-of-the-art models: FLUX.2-dev, HunyuanImage-3.0, and Qwen-Image-2512. The dataset enables systematic evaluation of AI-generated image detection, particularly focusing on out-of-distribution (OOD) generalization. Testing 17 classification models, the study finds high accuracy on images from seen generators but significant performance drops on images from unseen generators, underscoring persistent challenges in cross-generator generalization.
Why it matters: GenSyn10 provides a controlled benchmark for advancing research on the robustness and generalization of synthetic image detection systems, which is crucial for addressing AI-generated disinformation.
Full story at: arXiv Computer Vision ↗