发表机构
The University of Western Australia（西澳大利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对生成式AI输出检测难的问题，引入GenSyn10数据集，由三种模型生成图像。通过标准化协议整理数据，评估17个分类模型，结果显示CIFAR-10训练模型在该数据集上有一定准确率，确立其为合成图像检测基准，助力相关研究。
AI 中文摘要
生成式人工智能的快速发展使得可靠检测其输出变得困难，特别是当检测器遇到之前未见过的生成器时。我们引入了GenSyn10，这是一个与CIFAR-10对齐的合成图像数据集，包含60000张图像（10个类别，32×32，50k/10k分割），由三种架构不同的先进模型生成。GenSyn10通过标准化生成协议从多个当代架构中整理数据，解决了检测器在已知生成器上表现良好但在未知生成器上性能下降的问题。我们使用基于模板的提示引擎生成图像并进行下采样以确保一致性。在四个阶段的协议下评估了17个图像分类模型，结果表明CIFAR-10训练的模型在GenSyn10上零样本准确率高达96.86%，微调后提高到99.88%。在二元真实与合成分类中，微调后的模型在已知生成器上准确率为97-99.9%，但在未知生成器的图像上降至79-96%。这些结果确立了GenSyn10作为研究合成图像检测的受控基准，支持对鲁棒性、域适应和跨生成器泛化的研究。
英文摘要
The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before. We introduce GenSyn10, a CIFAR-10-aligned synthetic image dataset of 60,000 images (10 classes, 32$\times$32, 50k/10k split) generated using three architecturally diverse state-of-the-art models: FLUX.2-dev (Rectified Flow Transformer), HunyuanImage-3.0 (MoE Transformer), and Qwen-Image-2512 (Multimodal Diffusion Transformer), to advance research in AI-generated image detection. A central challenge in this domain is that detectors perform well on known generators but degrade on unseen ones. GenSyn10 addresses this limitation by curating data from multiple contemporary architectures under a standardized generation protocol, enabling controlled and systematic evaluation of out-of-distribution (OOD) generalization to novel generators. Images are generated using a template-based prompt engine and downsampled to ensure consistency. We evaluate 17 image classification models under a four-stage protocol: real-data baseline, zero-shot transfer, fine-tuning, and retention. Despite a measurable domain gap, CIFAR-10-trained models achieve up to 96.86\% zero-shot accuracy on GenSyn10, increasing to 99.88\% after fine-tuning. In binary real-vs-synthetic classification, fine-tuned models achieve 97-99.9\% accuracy on seen generators but drop to 79-96\% on images from an unseen generator, highlighting persistent limitations in OOD generalization. These results establish GenSyn10 as a controlled benchmark for studying synthetic image detection beyond single-generator settings, supporting research on robustness, domain adaptation, and cross-generator generalization.
