{"ID":2859246,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05740","arxiv_id":"2510.05740","title":"Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect","abstract":"The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images. Although most of the work has now focused on cross-generator generalization, we argue that this viewpoint is too limited. Detecting synthetic images involves another equally important challenge: generalization across visual domains. To bridge this gap,we present the OmniGen Benchmark. This comprehensive evaluation dataset incorporates 12 state-of-the-art generators, providing a more realistic way of evaluating detector performance under realistic conditions. In addition, we introduce a new method, FusionDetect, aimed at addressing both vectors of generalization. FusionDetect draws on the benefits of two frozen foundation models: CLIP \u0026 Dinov2. By deriving features from both complementary models,we develop a cohesive feature space that naturally adapts to changes in both thecontent and design of the generator. Our extensive experiments demonstrate that FusionDetect delivers not only a new state-of-the-art, which is 3.87% more accurate than its closest competitor and 6.13% more precise on average on established benchmarks, but also achieves a 4.48% increase in accuracy on OmniGen,along with exceptional robustness to common image perturbations. We introduce not only a top-performing detector, but also a new benchmark and framework for furthering universal AI image detection. The code and dataset are available at http://github.com/amir-aman/FusionDetect","short_abstract":"The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images. Although most of the work has now focused on cross-generator generalization, we argue that this viewpoint is too limited. Detecting synthetic images involves another equally import...","url_abs":"https://arxiv.org/abs/2510.05740","url_pdf":"https://arxiv.org/pdf/2510.05740v1","authors":"[\"Amirtaha Amanzadi\",\"Zahra Dehghanian\",\"Hamid Beigy\",\"Hamid R. Rabiee\"]","published":"2025-10-07T10:01:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":608622,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2859246,"paper_url":"https://arxiv.org/abs/2510.05740","paper_title":"Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect","repo_url":"https://github.com/amir-aman/FusionDetect","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}