GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification
GenSyn10 is a 60,000-image synthetic dataset aligned with CIFAR-10, generated by three architecturally diverse models to advance AI-generated image detection. Evaluation shows detectors perform well on known generators but degrade significantly on unseen ones, highlighting OOD generalization limitations.
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[Submitted on 10 Jul 2026]
Title:GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.16283 [cs.CV]
(or arXiv:2607.16283v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.16283
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md Faraz Kabir Khan [view email] [v1] Fri, 10 Jul 2026 09:47:16 UTC (3,184 KB)
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